1) The document describes plans for a new reactions database being developed by the Royal Society of Chemistry (RSC) to capture chemical reaction data from RSC publications.
2) The database will contain information on compounds, reactions, procedures and equipment to fully describe reactions at a level that allows others to reproduce them. Reactions will be captured with details on reaction components, yields, and characterization data.
3) An example reaction is provided that was text-mined from an RSC publication. It demonstrates how the reaction, compounds, procedure, and reference will be represented in the database following established standards like S88 procedures.
FIBER OPTIC AIDED SPECTROPHOTOMETRIC DETERMINATION OF GADOLINIUM IN FBR REPRO...ijac123
A new spectrophotometric method has been developed for the quantitative analysis of gadolinium using 1,2-dihydroxy anthraquinone-3-sulphonic acid, sodium salt (Alizarin Red S). Influence of various parameters such as concentration of complexing agent, pH, and interference of other competing metal ions was examined in a systematic manner.
Kinetics and Thermodynamics of Mandelic Acid Oxidation By Tripropylammonium H...inventionjournals
Two new Versatile reagent Tripropylammonium Fluorochromate(TriPAFC) and Tripropylammonium Chlorochromate(TriPACC) has been employed for the oxidation of Mandelic acid. Rate of the réaction is catalysed by surfactant Sodium LaurylEther Sulphate(SLES). The Oxidation has been studied spectrophotometrically at room température in perchloric acid medium. Rate of TriPAFC and TriPACC oxidation of Mandelic acid has been followed under pseudo-first order condition. Rate constants were calculated by the integrated rate equation. The graph of logk versus time was linear and the slope is near to unity, rate constant calculated from graph also agreed with experimental value shows the first order rate dépendance on Mandelic acid. Product formed in this oxidation of Mandelic acid was analysed, polymerization test were carried to confirm that the reaction follows ionic mechanism and stoichiometric data has been accounted.Temperature of the substrate is varried and from the rate constant value, Thermodynamic parameters like Activation energy, Enthalpy change, Entropy change and change in Gibb’s free energy is calculated using Arrhenius and Eyrings plot. All the kinetic runs were repeated and the rate constants were reproducible within ±2% range.
Solvent Extraction Method for the Separation of Cerium(III) as
Cations From Aqueous Media By use 4-[N-(5-methyl isoxazol-3-
yl)benzene sulfonamide azo]-1- Naphthol Coupled With
Spectroscopic Method For Determination
Non-Extractive Spectrophotometric Determination of Palladium in Biological Sa...IJAEMSJORNAL
A very simple, highly selective and non-extractive spectrophotometric method for the trace amounts of palladium(II) has been developed. Pyridoxal thiosemicarbazone (PTSC) has been proposed as a new analytical reagent for the direct non-extractive spectrophotometric determination of palladium (II). The reagent reacts with palladium in acidic medium(pH 2.0, CH3COONa and Con. HCl ) to form a pale yellow coloured 1: 2 (M : L) complex. The reaction is instantaneous and the maximum absorption was obtained at 420 nm and remains stable for 2 hrs. The molar absorptivity and sandell’s sensitivity were found to be 1.63 x 104 L mol-1 cm-1 and 0.635 μg cm-2 respectively. Linear calibration graphs were obtained for 0.9- 10.0 μg/ml of palladium(II). The method is highly selective for palladium and successfully used for determination of palladium in various hydrogenation catalysts.
FIBER OPTIC AIDED SPECTROPHOTOMETRIC DETERMINATION OF GADOLINIUM IN FBR REPRO...ijac123
A new spectrophotometric method has been developed for the quantitative analysis of gadolinium using 1,2-dihydroxy anthraquinone-3-sulphonic acid, sodium salt (Alizarin Red S). Influence of various parameters such as concentration of complexing agent, pH, and interference of other competing metal ions was examined in a systematic manner.
Kinetics and Thermodynamics of Mandelic Acid Oxidation By Tripropylammonium H...inventionjournals
Two new Versatile reagent Tripropylammonium Fluorochromate(TriPAFC) and Tripropylammonium Chlorochromate(TriPACC) has been employed for the oxidation of Mandelic acid. Rate of the réaction is catalysed by surfactant Sodium LaurylEther Sulphate(SLES). The Oxidation has been studied spectrophotometrically at room température in perchloric acid medium. Rate of TriPAFC and TriPACC oxidation of Mandelic acid has been followed under pseudo-first order condition. Rate constants were calculated by the integrated rate equation. The graph of logk versus time was linear and the slope is near to unity, rate constant calculated from graph also agreed with experimental value shows the first order rate dépendance on Mandelic acid. Product formed in this oxidation of Mandelic acid was analysed, polymerization test were carried to confirm that the reaction follows ionic mechanism and stoichiometric data has been accounted.Temperature of the substrate is varried and from the rate constant value, Thermodynamic parameters like Activation energy, Enthalpy change, Entropy change and change in Gibb’s free energy is calculated using Arrhenius and Eyrings plot. All the kinetic runs were repeated and the rate constants were reproducible within ±2% range.
Solvent Extraction Method for the Separation of Cerium(III) as
Cations From Aqueous Media By use 4-[N-(5-methyl isoxazol-3-
yl)benzene sulfonamide azo]-1- Naphthol Coupled With
Spectroscopic Method For Determination
Non-Extractive Spectrophotometric Determination of Palladium in Biological Sa...IJAEMSJORNAL
A very simple, highly selective and non-extractive spectrophotometric method for the trace amounts of palladium(II) has been developed. Pyridoxal thiosemicarbazone (PTSC) has been proposed as a new analytical reagent for the direct non-extractive spectrophotometric determination of palladium (II). The reagent reacts with palladium in acidic medium(pH 2.0, CH3COONa and Con. HCl ) to form a pale yellow coloured 1: 2 (M : L) complex. The reaction is instantaneous and the maximum absorption was obtained at 420 nm and remains stable for 2 hrs. The molar absorptivity and sandell’s sensitivity were found to be 1.63 x 104 L mol-1 cm-1 and 0.635 μg cm-2 respectively. Linear calibration graphs were obtained for 0.9- 10.0 μg/ml of palladium(II). The method is highly selective for palladium and successfully used for determination of palladium in various hydrogenation catalysts.
Diazo coupling for the determination of selexipag by visible spectrophotometryRatnakaram Venkata Nadh
Aim and Objective: The aim and objective of this study were to develop a spectrophotometric method for the assay of selexipag (selective IP prostacyclin receptor agonist indicated for the treatment of pulmonary arterial hypertension) in pure and pharmaceutical formulations so that it will be an alternative quantitative method to chromatographic methods which require large quantities of organic solvents, where some are with hazardous and toxic properties. Materials and Methods: The method is based on the diazo coupling of selexipag with diazotized p-nitroaniline in alkaline medium to form a stable green-colored and water-soluble azo dye with a maximum absorption at 510 nm. Optimization of reaction conditions was carried out to get highly sensitive and stable colored complex. Results and Discussion: Beer’s law is obeyed over the concentration range of 2–12 μg/mL with a molar absorptivity of 3.33 × 104 L/mol/cm. The limit of detection was 0.35 μg/mL and limit of quantification was 1.0 μg/mL. The results demonstrated that the procedure is accurate, precise, and reproducible (relative standard deviation <2%). Conclusions: This method was tested and validated for various parameters according to the current ICH guidelines.
Antibacterial Application of Novel Mixed-Ligand Dithiocarbamate Complexes of ...IOSR Journals
Nine stable mixed ligand dithiocarbamate complexes of Nickel (II) ion were prepared. The complexes were characterized with electronic spectroscopy, infrared spectroscopy, conductance measurement, melting point and percentage metal analysis. Resulting analytical data gave credence to the assignment of a tentative square planar geometry to all the complexes. The complexes were proposed to have a general formulae of [Ni(Sal)(Rdtc)], where Sal = salicylaldehyde; R = dibenzylamine(Bz2NH), methylphenylamine(MePhNH),pyrrolidineamine(pyrrolNH),piperidineamine(piperNH),morpholineamine(MorpNH), anilineamine(AnilNH), para-chloroanilineamine(p-ClAnilNH), toludineamine(TolNH) and anisidineamine(AnisNH); and dtc = dithiocarbamate anion. The metal complexes were screened against six different bacteria strain using Agar diffusion method. The antibacterial studies reveal that the metal complexes exhibit broad spectrum antibacterial activity against Escherichia coli, Staphylococcus aureus, Klebsiella oxytoca and Pseudomonas aureginosa with inhibitory range of 10.5.—20.0mm.
New Analytical Technique For The Determination Of Mercury (II) By Synergistic...inventionjournals
A new technique was developed for the extractive spectrophotometric determination of mercury (II) by using newly synthesized chromogenic reagent N'',N'''-bis[(E)-(4-fluorophenyl)methylidene] thiocarbonohydrazide bis-(4-fluoroPM)TCH. It forms yellow colored ternary complex with mercury(II) in presence pyridine having composition 1:1:1 (M:Reagent:Py) in acidic pH range 1.7-3.7. The reagent is highly sensitive and selective towards mercury(II). So spectrophotometric method of mercury(II) is found to be very rapid, reliable and show synergistic effect. Absorption of colored organic layer in iso amyl acetate is measured with reagent blank at λmax 375 nm. Pyridine showed synergistic effect with reagent by the adduct formation in organic phase. Beer’s law was obeyed in the concentration range 0.25 to 3.5 µg mL-1 for mercury (II). Molar absorptivity and sandell’s sensitivity values of mercury(II)-bis-(4-fluoroPM)TCH-Py complex are 0.50127x105 lit mol-1 cm -1 and 0.004 µg cm -2 , respectively. The selectivity of the method was checked by using various foreign ions. The composition of the complex was determined by slope ratio method, mole ratio method and Job’s method of continuous variation. The colour of ternary complex was stable for more than 12 h. Various factors influencing on degree of comlexation are the effect of pH, reagent concentration, synergent concentration, equilibrium time, solvent were determined. The method was applicable for determination of mercury(II) in binary mixture, ternary mixture, ayurvedic sample, homoeopathic sample, industrial waste water, spiked water and dental unit waste water.
Adsorption Characteristics and Behaviors of Natural Red Clay for Removal of B...ijtsrd
The present study deals with the analysis and adsorption of Basic Yellow 28 BY28 onto low-cost natural red clay NRC . Adsorbent characterized by XRD, SEM, TG DTA, BET and BJH. The effect of the contact time, the temperature, the initial concentration, the pH and the adsorbent mass and on adsorption process were investigated using by batch adsorption technique and then the adsorption isotherm, kinetics, thermodynamics and equilibrium studies were performed. The pH effect on the removal of BY28 efficiency was not important. It was found that the isotherm model best suited to the equilibrium data obtained from the adsorption of BY28 on NRC was the pseudo-second order. It was found that the kinetic model best suited to the data obtained from the adsorption of BY28 on NRC was the Langmuir model. The maximum monolayer adsorption capacity was 370 mg. g-1. In the thermodynamic studies, it can be said that the adsorption of BY28 onto NRC takes place spontaneously, physically and endothermic ally. Finally, the use of NRC shows a greater potential for the removal of cationic dyes, as no costly equipment is required. Omer Lacin | Ali Haghighatnia | Fatih Demir | Fatih Sevim "Adsorption Characteristics and Behaviors of Natural Red Clay for Removal of BY28 from Aqueous Solutions" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2 , February 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21544.pdf
Paper URL: https://www.ijtsrd.com/engineering/engineering-chemistry/21544/adsorption-characteristics-and-behaviors-of-natural-red-clay-for-removal-of-by28-from-aqueous-solutions/omer-lacin
Measuring pKas, logP and Solubility by Automated titrationJon Mole
Presentation by Sirius Analytical covering measurement of pKa, LogP, LogD, Solubility, Supersaturation and precipitation kinetics.
For more details visit www.sirius-analytical.com
N-alkylation methods, Characterization and Evaluation of antibacterial activi...IJERA Editor
A series of new 5-Chloroisatin derivates have been synthesized by the method of N-alkylation at room temperature, in the presence of a base and a catalyst with good yields. The chemical structures of these compounds were confirmed by NMR (1H &13C), these new compounds obtained were evaluated for their antibacterial activity. The final results revealed that the majority of the compounds exhibited good antimicrobial activity against various organisms
Some physicochemical properties such as density, refractive index, solubility, conductance, dissociation constant etc. have been studied for some newly synthesized chalcones in different solvents at 308.15 K.
The presentation covers simple methods to learn separation of organic binary mixtures, and also guides beginners of organic chemistry to build on their knowledge on minute aspects of organic chemistry. Tips regarding safety precautions to be taken in the organic chemistry laboratory are also given.
Determination of Satranidazole through Ion-Associative Complex ReactionRatnakaram Venkata Nadh
A simple, selective, accurate and low-cost spectrophotometric method
has been described for determination of satranidazole in bulk and
pharmaceutical formulations. The developed method involves the
formation of chloroform extractable colored ion-association complex
of satranidazole with Tropaeolin OOO (TPooo). The extracted colored
complex showed absorbance maximum at wavelength 484 nm and
obeying Beer′s law in the concentration 4-20 μg mL-1 with the
correlation coeffiecent of 0.9998. The results of statistical analysis of
the proposed method reveals high accuracy and good precession. Thus,
the proposed method can be used commercially for the determination
of satranidazole in bulk and pharmaceutical formulations.
Synthesis, Characterization and Biological Evaluation of Substitutedthiazolid...paperpublications3
Abstract: A new series of substituted thiazolidin-4-ones were synthesized and evaluated for anticancer activity by means of MTT assay method for improved anticancer activity .The structures of these synthesized compounds were established by means of IR,H NMR analysis.All the compounds were evaluated for their anticancer activity .Compounds TH10 & TH19 were found most active due to presence of electron withdrawing groups at appropriate position.
Experiences and adventures with no sql and its applications to cheminformatic...Valery Tkachenko
The Royal Society of Chemistry hosts an increasing number of chemistry related databases and have utilized SQL-based technologies for our development platforms in general. In recent years the interest in noSQL databases has exploded as the associated technologies have developed and have shown great promise in terms of enhanced performance. We have collaborated with GGA Software Services to implement their noSQL technologies and have integrated it into the compound repository presently being developed as part of the underpinning architecture for compound data management at the RSC. This presentation will provide an overview of the reasons why we have integrated a noSQL solution, quantitative analysis of the benefits of inclusion and our thoughts regarding further approaches to optimize search performance for the chemical compound repository.
UCSD NANO 266 Quantum Mechanical Modelling of Materials and Nanostructures is a graduate class that provides students with a highly practical introduction to the application of first principles quantum mechanical simulations to model, understand and predict the properties of materials and nano-structures. The syllabus includes: a brief introduction to quantum mechanics and the Hartree-Fock and density functional theory (DFT) formulations; practical simulation considerations such as convergence, selection of the appropriate functional and parameters; interpretation of the results from simulations, including the limits of accuracy of each method. Several lab sessions provide students with hands-on experience in the conduct of simulations. A key aspect of the course is in the use of programming to facilitate calculations and analysis.
Diazo coupling for the determination of selexipag by visible spectrophotometryRatnakaram Venkata Nadh
Aim and Objective: The aim and objective of this study were to develop a spectrophotometric method for the assay of selexipag (selective IP prostacyclin receptor agonist indicated for the treatment of pulmonary arterial hypertension) in pure and pharmaceutical formulations so that it will be an alternative quantitative method to chromatographic methods which require large quantities of organic solvents, where some are with hazardous and toxic properties. Materials and Methods: The method is based on the diazo coupling of selexipag with diazotized p-nitroaniline in alkaline medium to form a stable green-colored and water-soluble azo dye with a maximum absorption at 510 nm. Optimization of reaction conditions was carried out to get highly sensitive and stable colored complex. Results and Discussion: Beer’s law is obeyed over the concentration range of 2–12 μg/mL with a molar absorptivity of 3.33 × 104 L/mol/cm. The limit of detection was 0.35 μg/mL and limit of quantification was 1.0 μg/mL. The results demonstrated that the procedure is accurate, precise, and reproducible (relative standard deviation <2%). Conclusions: This method was tested and validated for various parameters according to the current ICH guidelines.
Antibacterial Application of Novel Mixed-Ligand Dithiocarbamate Complexes of ...IOSR Journals
Nine stable mixed ligand dithiocarbamate complexes of Nickel (II) ion were prepared. The complexes were characterized with electronic spectroscopy, infrared spectroscopy, conductance measurement, melting point and percentage metal analysis. Resulting analytical data gave credence to the assignment of a tentative square planar geometry to all the complexes. The complexes were proposed to have a general formulae of [Ni(Sal)(Rdtc)], where Sal = salicylaldehyde; R = dibenzylamine(Bz2NH), methylphenylamine(MePhNH),pyrrolidineamine(pyrrolNH),piperidineamine(piperNH),morpholineamine(MorpNH), anilineamine(AnilNH), para-chloroanilineamine(p-ClAnilNH), toludineamine(TolNH) and anisidineamine(AnisNH); and dtc = dithiocarbamate anion. The metal complexes were screened against six different bacteria strain using Agar diffusion method. The antibacterial studies reveal that the metal complexes exhibit broad spectrum antibacterial activity against Escherichia coli, Staphylococcus aureus, Klebsiella oxytoca and Pseudomonas aureginosa with inhibitory range of 10.5.—20.0mm.
New Analytical Technique For The Determination Of Mercury (II) By Synergistic...inventionjournals
A new technique was developed for the extractive spectrophotometric determination of mercury (II) by using newly synthesized chromogenic reagent N'',N'''-bis[(E)-(4-fluorophenyl)methylidene] thiocarbonohydrazide bis-(4-fluoroPM)TCH. It forms yellow colored ternary complex with mercury(II) in presence pyridine having composition 1:1:1 (M:Reagent:Py) in acidic pH range 1.7-3.7. The reagent is highly sensitive and selective towards mercury(II). So spectrophotometric method of mercury(II) is found to be very rapid, reliable and show synergistic effect. Absorption of colored organic layer in iso amyl acetate is measured with reagent blank at λmax 375 nm. Pyridine showed synergistic effect with reagent by the adduct formation in organic phase. Beer’s law was obeyed in the concentration range 0.25 to 3.5 µg mL-1 for mercury (II). Molar absorptivity and sandell’s sensitivity values of mercury(II)-bis-(4-fluoroPM)TCH-Py complex are 0.50127x105 lit mol-1 cm -1 and 0.004 µg cm -2 , respectively. The selectivity of the method was checked by using various foreign ions. The composition of the complex was determined by slope ratio method, mole ratio method and Job’s method of continuous variation. The colour of ternary complex was stable for more than 12 h. Various factors influencing on degree of comlexation are the effect of pH, reagent concentration, synergent concentration, equilibrium time, solvent were determined. The method was applicable for determination of mercury(II) in binary mixture, ternary mixture, ayurvedic sample, homoeopathic sample, industrial waste water, spiked water and dental unit waste water.
Adsorption Characteristics and Behaviors of Natural Red Clay for Removal of B...ijtsrd
The present study deals with the analysis and adsorption of Basic Yellow 28 BY28 onto low-cost natural red clay NRC . Adsorbent characterized by XRD, SEM, TG DTA, BET and BJH. The effect of the contact time, the temperature, the initial concentration, the pH and the adsorbent mass and on adsorption process were investigated using by batch adsorption technique and then the adsorption isotherm, kinetics, thermodynamics and equilibrium studies were performed. The pH effect on the removal of BY28 efficiency was not important. It was found that the isotherm model best suited to the equilibrium data obtained from the adsorption of BY28 on NRC was the pseudo-second order. It was found that the kinetic model best suited to the data obtained from the adsorption of BY28 on NRC was the Langmuir model. The maximum monolayer adsorption capacity was 370 mg. g-1. In the thermodynamic studies, it can be said that the adsorption of BY28 onto NRC takes place spontaneously, physically and endothermic ally. Finally, the use of NRC shows a greater potential for the removal of cationic dyes, as no costly equipment is required. Omer Lacin | Ali Haghighatnia | Fatih Demir | Fatih Sevim "Adsorption Characteristics and Behaviors of Natural Red Clay for Removal of BY28 from Aqueous Solutions" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2 , February 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21544.pdf
Paper URL: https://www.ijtsrd.com/engineering/engineering-chemistry/21544/adsorption-characteristics-and-behaviors-of-natural-red-clay-for-removal-of-by28-from-aqueous-solutions/omer-lacin
Measuring pKas, logP and Solubility by Automated titrationJon Mole
Presentation by Sirius Analytical covering measurement of pKa, LogP, LogD, Solubility, Supersaturation and precipitation kinetics.
For more details visit www.sirius-analytical.com
N-alkylation methods, Characterization and Evaluation of antibacterial activi...IJERA Editor
A series of new 5-Chloroisatin derivates have been synthesized by the method of N-alkylation at room temperature, in the presence of a base and a catalyst with good yields. The chemical structures of these compounds were confirmed by NMR (1H &13C), these new compounds obtained were evaluated for their antibacterial activity. The final results revealed that the majority of the compounds exhibited good antimicrobial activity against various organisms
Some physicochemical properties such as density, refractive index, solubility, conductance, dissociation constant etc. have been studied for some newly synthesized chalcones in different solvents at 308.15 K.
The presentation covers simple methods to learn separation of organic binary mixtures, and also guides beginners of organic chemistry to build on their knowledge on minute aspects of organic chemistry. Tips regarding safety precautions to be taken in the organic chemistry laboratory are also given.
Determination of Satranidazole through Ion-Associative Complex ReactionRatnakaram Venkata Nadh
A simple, selective, accurate and low-cost spectrophotometric method
has been described for determination of satranidazole in bulk and
pharmaceutical formulations. The developed method involves the
formation of chloroform extractable colored ion-association complex
of satranidazole with Tropaeolin OOO (TPooo). The extracted colored
complex showed absorbance maximum at wavelength 484 nm and
obeying Beer′s law in the concentration 4-20 μg mL-1 with the
correlation coeffiecent of 0.9998. The results of statistical analysis of
the proposed method reveals high accuracy and good precession. Thus,
the proposed method can be used commercially for the determination
of satranidazole in bulk and pharmaceutical formulations.
Synthesis, Characterization and Biological Evaluation of Substitutedthiazolid...paperpublications3
Abstract: A new series of substituted thiazolidin-4-ones were synthesized and evaluated for anticancer activity by means of MTT assay method for improved anticancer activity .The structures of these synthesized compounds were established by means of IR,H NMR analysis.All the compounds were evaluated for their anticancer activity .Compounds TH10 & TH19 were found most active due to presence of electron withdrawing groups at appropriate position.
Experiences and adventures with no sql and its applications to cheminformatic...Valery Tkachenko
The Royal Society of Chemistry hosts an increasing number of chemistry related databases and have utilized SQL-based technologies for our development platforms in general. In recent years the interest in noSQL databases has exploded as the associated technologies have developed and have shown great promise in terms of enhanced performance. We have collaborated with GGA Software Services to implement their noSQL technologies and have integrated it into the compound repository presently being developed as part of the underpinning architecture for compound data management at the RSC. This presentation will provide an overview of the reasons why we have integrated a noSQL solution, quantitative analysis of the benefits of inclusion and our thoughts regarding further approaches to optimize search performance for the chemical compound repository.
UCSD NANO 266 Quantum Mechanical Modelling of Materials and Nanostructures is a graduate class that provides students with a highly practical introduction to the application of first principles quantum mechanical simulations to model, understand and predict the properties of materials and nano-structures. The syllabus includes: a brief introduction to quantum mechanics and the Hartree-Fock and density functional theory (DFT) formulations; practical simulation considerations such as convergence, selection of the appropriate functional and parameters; interpretation of the results from simulations, including the limits of accuracy of each method. Several lab sessions provide students with hands-on experience in the conduct of simulations. A key aspect of the course is in the use of programming to facilitate calculations and analysis.
The Open PHACTS project delivers an online platform integrating a wide variety of data from across chemistry and the life sciences and an ecosystem of tools and services to query this data in support of pharmacological research, turning the semantic web from a research project into something that can be used by practising medicinal chemists in both academia and industry. In the summer of 2015 it was the first winner of the European Linked Data Award. At the Royal Society of Chemistry we have provided the chemical underpinnings to this system and in this talk we review its development over the past five years. We cover both our early work on semantic modelling of chemistry data for the Open PHACTS triplestore and more recent work building an all-purpose data platform, for which the Open PHACTS data has been an important test case, what has worked well, what's missing and where this is is likely to go in future.
Text mining to produce large chemistry datasets for community accessValery Tkachenko
While in an ideal world all data would be deposited by the producing scientist directly into a database, in the real-world most chemical data is instead presented in a form designed for human rather than machine consumption. Text mining has the potential to extract this data back into a computer understandable form. As all United States patents are available free of charge they make the perfect corpus for extracting a large number of experimental properties of compounds, and chemical reactions.
We report on our text-mining activities to extract millions of textual NMR spectra, hundreds of thousands of physicochemical properties (with their associated compounds) and over a million chemical reactions. All extracted results are to be deposited into online databases allowing the community to benefit from the results of this work.
Using Mestrelab Research’s MNova product we have converted the textual NMR spectra to graphical spectra, and validated each spectrum against its associated chemical structure so as to detect cases where the NMR spectrum could not be produced by the associated structure.
In the case of melting points the resultant dataset, of over a quarter of a million melting compound/temperature relationships, is the largest public dataset the authors are aware of. We have used this dataset to produce a predictive model with results comparable to those of manually curated datasets. Our experiences with modelling this data has demonstrated that we are working at the edge of current algorithmic and computing capabilities for predictive model building, with the resultant matrix containing over 200 billion descriptors. The melting point model and the data it was derived from are available freely from http://www.ochem.eu.
Imrokraft Solutions Pvt Ltd is one of best center for java training in trivandrum, Kerala. We provide training in advanced java training in trivandrum, Kerala. We also provide android training in trivandrum, php training in trivandrum, angularjs training in trivandrum, angularjs training in technopark, trivandrum, web designing training in technopark, trivandrum. Our Website is http://imrokraft.com. Contact us at: 04716555644, 6555744.
From Employee to Advocate: Amplify your talent brand through employee engage...Rebecca Feldman
On average, a company’s employees have 10 times as many connections as a company has followers! So what better way to amplify your talent brand message than through your own employees?
This presentation, from a LinkedIn Talent Brand Workshop, teaches you how to turn your employees into advocates for your talent brand and the impact it can have on your organization. You will learn about promoting your brand internally, boosting referrals, social media advocacy, facilitating employee created content, and more.
This is a presentation I made to the LSSC10 audience on Friday 4/23/2010.
I speak of my observations of individuals or groups of individuals who are agents of change. I call them INFLUENCERS.
Please do let me know your feedback.
Webinar: Mobile Marketing for Health ClubsNetpulse
The old ways of club marketing are rapidly dying. Luckily, the surging growth of smartphones has created an exciting new way to acquire more members and sell more services at clubs. It’s called Mobile Marketing. See it here: http://offers.netpulse.com/webinar-mobile-marketing-for-health-clubs
Sustainable research progress in many scientific disciplines critically depends on the existence of robust specialized databases that integrate and structure all available experimental information in the respective fields. Over years a multitude of chemical formats and approaches were created to address various aspects of handling chemical information and building databases of chemical knowledge. Additional to that inconsistencies in data formatting by individual labs leads to the need to invest significant resources in data curation and interpretation by the technical staff involved in the maintenance of the centralized data collection resource. Acquisition of data from public sources is inefficient, time consuming and limited in scope. The NIH has recently posted its intention to financially support data deposition by investigators through the ‘data sharing plan' for each funded proposal. However, this plan also points to a current weakness of the centralized data sharing and acquisition as all laboratories use different data collection and formatting approaches. It would be far more efficient and useful if there were a standardized data collection and deposition template with standard key terms that could be modified to add new or important additional data or parameters for each investigator. These new features could be ultimately adopted in the classification scheme and guide the scope of the expanding database. This approach would be a win-win as it would enable structure for the investigators laboratory, consistency in data reporting and a means of transmitting data to the database in parallel to publication to eliminate the acquisition step from the process. In this talk we will outline our experience building Open Data Science Platform, a federated database system for direct acquisition, curation and management of research data with integrated Machine Learning capabilities.
ABSTRACT- L-Ascorbic acid derivatives was synthesized on treatment with acetone and acetyl chloride afforded 5,6-acetal of L-ascorbic acid then benzylation of C-2 and C-3 hydroxyl groups of the lactone ring was accomplished using K2CO3 and benzyl bromide in DMF, then deblocking of the 5,6-O,O-protected derivative of L-Ascorbic acid with acetic acid and methanol gave 2,3-O,O-dibenzyl-L-Ascorbic acid. Subsequently mono-tosylation at 6 position of 2,3-O, O-dibenzyl-L-Ascorbic acid was carried out with addition of p-toluenetosylchloride (PTSC) in Pyridine and MDC solvent medium gave 2,3-O,O-dibenzyl-6-O-tosyl-L-Ascorbic acid. All the structures were characterized by 1H NMR, 13C NMR and Mass Spectroscopy.
Key-words- L-Ascorbic acid, 5,6-Acetal, Benzylation, Hydrolysis, Tosylation
Syntheses and Characterizations of Some New N-alkyl, Isoxazole and Dioxazole ...IJAEMSJORNAL
N-alkyl and cycloadducts derivatives of 5-Chloroisatin were synthesized in good to excellent yields. The method evidences a selective N-alkylation when using 1,2-bis (2-chloroethoxy) ethane as efficient spacer at room temperature on the 5-Chloroisatin moiety. A general method for the 1,3-dipolar cycloaddition of 4-Chlorobenzaldoxime to alkynes provides a useful alternative route to get newisoxazole et dioxazole derivatives.
Synthesis of 1,2,3-Triazole 5-Chloroisatin Derivatives via Copper-Catalyzed 1...IJAEMSJORNAL
A facile and simple protocol for the ‘Click’ cycloaddition of organic azides with N-propargylchloroisatine catalyzed by CuI, produces in good yields novel of 1,4-disubstituted 1,2,3-triazoles were obtained. Compared to the uncatalyzed cycloaddition, the yields are significantly improved in the presence of CuI as catalyst, without alteration of the selectivity. The regio- and stereochemistry of the cycloadducts has been corroborated by 1H, 13C NMR spectroscopy.
Parameter Estimation of Pollutant Removal for Subsurface Horizontal Flow Cons...mkbsbs
Treatment efficiencies of a pilot scale constructed wetland treating greywater
from a staff canteen of the University of Moratuwa was studied to estimate the
temperature dependent reaction rate constants of specific pollutant removal
mechanisms.
Research Inventy : International Journal of Engineering and Scienceresearchinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
CHE235L4Spring2017.pdf
FW
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o
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C) mmol mass (g)
density
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volume
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N/A
bismuth(III) nitrate pentahydrate N/A N/A N/A N/A
sodium chloride, saturated (brine) N/A N/A N/A N/A N/A
ethyl acetate N/A N/A
cis -1,2-cyclohexanediol N/A N/A N/A
trans -1,2-cyclohexanediol, (±) N/A N/A N/A
Prelab 4: Green Lewis Acid-Catalyzed Hydrolysis of Cyclohexene Oxide
Name:
Reaction equation:
Note: For those reagents that are in solution, the FW, mmol, and mass columns refer to the solute in the
solution.
Limiting reagent:
Reagent Table
water
Theoretical yield:
Chemical
cyclohexene oxide
EXPERIMENT #4
GREEN LEWIS ACID-CATALYZED HYDROLYSIS OF CYCLOHEXENE OXIDE
Introduction:
Epoxides are three-membered ethers. They are special because unlike most ethers, they can react
with nucleophiles to form a new bond between carbon and the nucleophile and break a bond
between that carbon and oxygen. This ring-opening reaction makes epoxides versatile functional
groups for organic synthesis. (In fact epoxide is the functional group that makes epoxy resins
possible.)
Scheme 1. Ring opening of an epoxide in the presence of a nucleophile.
Ring-opening of the epoxide can occur under basic or acidic conditions. Under basic conditions,
the reaction is similar to an SN2 reaction so that the nucleophile attacks the less substituted carbon
of an unsymmetrical epoxide by backside attack. Sodium ethoxide reacts with this epoxide in the
following reaction.
Scheme 2. Ring opening of an unsymmetrical epoxide under basic conditions.
Under acidic conditions, the reaction is more complicated. It is similar to an SN2 reaction because
the nucleophile reacts by backside attack. However, because there is partial positive charge on the
Reference Material:
MAHHS Chapter 1: Safety in the Laboratory
MAHHS Chapter 2: Protecting the Environment
MAHHS Chapter 3: Laboratory Notebooks and Prelaboratory Information
MAHHS Chapter 4: Laboratory Glassware
MAHHS Chapter 5: Measurements and Transferring Reagents
MAHHS Chapter 10: Filtration
MAHHS Chapter 11: Extraction
MAHHS Chapter 12: Drying Organic Liquids and Recovering Reaction Products
MAHHS Chapter 17: Thin-Layer Chromatography, especially section 17.8
MAHHS Chapter 20: Infrared Spectroscopy
Klein Chapter 14: Ethers and Epoxides; Thiols and Sulfides
three atoms of the epoxide ring, the nucleophile attacks where the partial positive charge is more
stabilized, the more substituted carbon of an unsymmetrical epoxide. Ethanol in the presence of
sulfuric acid reacts with this epoxide in the following reaction.
Scheme 3. Ring opening of an unsymmetrical epoxide under acidic conditions.
While sulfuric acid is an inexpensive acid catalyst, it is difficult to handle. It is very corrosive and
can cause severe burns. In addition, it is viscous, which makes it difficult to handle on the scale of
the reactions perfor ...
Evolution of public chemistry databases: past and the futureValery Tkachenko
Over the last few years we have seen a tremendous growth in various chemical databases. As a result we have now a variety of scientific resources, combined into a broad network and indexed through the directories like BioSharing and re3data. Such network, while growing quickly, is still in early days of adopting semantic web standards and does not yet support deep data indexing and discoverability, leave alone that mechanisms of intellectual properties protection are as simple as making data public or private at best. The lack of standards and well defined models to describe a scientific information structure even further inhibits free information flow which is essential for scientific discovery.
In this talk we will share our experience spanning through decades of building chemical databases like PubChem, ChemSpider, OpenPHACTS and National Database Services and will outline fundamental problems associated with chemical databases as such as well as data quality and approaches for the modern architecture of the large-scale chemical databases.
Materials design is a grand challenge of materials science. And the main approach for solving this problem is still intuition-based. Such a way requires a lot of time and financial resources and months to years of conducting the experiment and doing characterization. Therefore, any kind of model that can be used at the very first stage of materials design and can narrow the selection area is a helpful tool for synthetic chemist. Also, an automated search for materials with human-defined target properties in the entire chemical space, i.e. inverse materials design is a highly desired tool in the exploration of materials design space.
Along with that, de novo design is not a kind of a completely new task in a field of development of new organic molecules with target properties. A lot of different generative approaches are being used along with screening the libraries of existing molecules, searching for drugs for a particular target, or generating new ones based on a very simple initial structure.
Here we would like to present a new approach for generating new materials with desired properties. We used autoencoder neural network architecture to encode materials composition and crystal structure as a vector in a latent space. In such case, any Quantitative Structure-Property Relationship (QSPR) model based on the vector can be interpreted as function in the latent space and can be used to predict property of existing materials as well as for prophetic ones. Such an approach has comparable accuracy with such classic computational methods as DFT in the case of predicting values of energies or charges, but significantly transcends them in terms of computational time.
The proposed method was tested for generating super-firm materials, but can easily be extended to any target properties, granted a database of materials properties can be provided for training.
Metal-organic frameworks: from database to supramolecular effects in complexa...Valery Tkachenko
Metal-organic frameworks (MOFs) attract a lot of interest due to their unique structure-dependent properties. Their internal pores comparable to the size of small molecules are naturally refined for various absorbance effects. Possessed properties lie in a foundation of multiple applications, such as catalysis, gas storage/separation and especially – clean energy related ones.
Theoretical calculations are a usual way of decreasing experimental costs while investigating properties of new materials, especially at a design stage. Electronic structure calculations like density functional theory (DFT) in most cases provide an appropriate accuracy in matching experimentally measured data such as adsorbate interaction energies. However, as in the case of experimental studies, large-scale materials screening studies with DFT calculations are rather time-consuming, and it can be carried out only for structures with relatively small unit cell.
Here we would like to present a theoretical and experimental results describing calculation of electron density in metal-organic frameworks. We built a model trained to predict partial charges on MOF atoms based on DFT calculations. The relative error of the model allows us to conclude that models do not decrease the level of accuracy and do not superinduce additional error comparing to DFT. At the same time, computational cost of the model is several orders of magnitude less. Models also demonstrated transferability and allowed to make prediction e.g. for MOFs containing metals not presented in the train set.
We have also built a force-field (FF) of two-centered and three-centered interatomic potentials constructed using predicted charges. The FF proved to reproduce MOF crystal structure. As a final test, we have applied the developed model and FF to a new synthesized lanthanide-containing MOFs to estimate influence of supramolecular effects on metal complexation selectivity.
As a result, we’ve built a model predicting one of basic MOF properties within relatively small computational time and tested it on experimental data, both obtained from literature sources and self-investigated.
Public repositories containing diverse chemical and biological data are one of the main sources of knowledge for further biomedical research. Unfortunately, extraction and transforming these data into a well-interpretable form is a complex exercise. Ongoing efforts of a community are mainly focused on the analysis of co-occurrences of terms, text annotation based on terms similarity and related tasks [1].
Here we present an approach based on natural-language processing techniques, which is intended to shift the focus of a search for similar texts on chemical topics from word- to document-level. PubMed records were used to implement word2vec and doc2vec models. Generated text representations can be used to search for similar abstracts; the similarity is more dependent on this representation than the co-presence of certain terms (neighbor compounds, similar publication date, etc.).
Document-level clustering was also implemented to provide insight into the PubMed text corpus structure. This approach can serve as an alternative to standard topic modeling techniques for the discovery of hidden semantic features in an unsupervised manner.
Machine learning methods for chemical properties and toxicity based endpointsValery Tkachenko
In the last decade there is an increasing interest in using in silico tools for potential risk assessment of newly released chemicals due to the large number of chemicals enter the market yearly and the big uncertainty on their possible hazardous effects. Different tools and methods based on machine learning techniques already exist and were used in a wide range of applications starting from quantitative structure-property relationships and expanding into predictive toxicology. There is a lot of historical data accumulated across multiple databases which is publicly available and can be used with novel machine learning methods. Unfortunately, due to different datasets, metrics and validation strategies, the significant gaps remain in both the quantity and quality of data available coupled with optimal predictive methods. This work is an attempt to develop a multitask system which can serve as searchable curated collections of multiple chemical datasets and ready to use novel machine learning methods solely built using open source frameworks and libraries. We have implemented a set of self-tuned, using grid search and k-fold validation, traditional machine learning methods (shallow methods) such as Naïve Bayes, k-Nearest Neighbors, Random Forest, Boosted Decision Trees, Regularized Logistic Regression, and Support Vector Machines base on open source Scikitlearn (http://scikit-learn.org/stable/). The novel Deep Neural Networks models of different complexity have been also implemented using Keras (https://keras.io/), a deep learning open library, and a Tensorflow (www.tensorflow.org) as a backend. The machine learning models were trained and evaluated to predict measures of toxicity from the physical characteristics of the structure of chemicals using the same datasets as in the Toxicity Estimation Software Tool (https://www.epa.gov/chemical-research/toxicity-estimation-software-tool-test). The Deep Learning models showed very good performance evaluation characteristics and were found to be useful in predicting of toxicological and physicochemical parameter endpoints. The results of this work support an optimistic view that some of current obstacles in cheminformatics can be overcome by using Deep Learning methods.
Chemical workflows supporting automated research data collectionValery Tkachenko
Acquisition of data from public sources is inefficient, time consuming and limited in scope. The NIH has recently posted its intention to financially support data deposition by investigators through the ‘data sharing plan' for each funded proposal. However, this plan also points to a current weakness of the centralized data sharing and acquisition as all laboratories use different data collection and formatting approaches. These inconsistencies in data formatting by individual labs leads to the need to invest significant resources in data curation and interpretation by the technical staff involved in the maintenance of the centralized data collection resource such as CaNanoLab or Nanomaterial Registry. It would be far more efficient and useful if there were a standardized data collection and deposition template with standard key terms (such as Minimal Information about Nanomaterials, MIAN) that could be modified to add new or important additional data or parameters for each investigator. These new features cold be ultimately adopted in the classification scheme and guide the scope of the expanding database. This approach would be a win-win as it would enable structure for the investigators laboratory, consistency in data reporting and a means of transmitting data to the database in parallel to publication to eliminate the acquisition step from the process. In this talk we will outline our experience building Open Science Data Repository, a federated database system for direct acquisition, curation and management of research data, including nanomaterial data capture, transformation, and streamlined submission to nanomaterial knowledgebases. The key part of the system is microservices based architecture which exposes RESTful API suitable for direct integration into Workflow Management Systems as well as built-in modules facilitating and enforcing various lab-specific standard operating procedures.
Deep learning methods applied to physicochemical and toxicological endpointsValery Tkachenko
Chemical and pharmaceutical companies, and government agencies regulating both chemical and biological compounds, all strive to develop new methods to provide efficient prioritization, evaluation and safety assessments for the hundreds of new chemicals that enter the market annually. While there is a lot of historical data available within the various agencies, organizations and companies, significant gaps remain in both the quantity and quality of data available coupled with optimal predictive methods. Traditional QSAR methods are based on sets of features (fingerprints) which representing the functional characteristics of chemicals. Unfortunately, due to both data gaps and limitations in the development of QSAR models, read-across approaches have become a popular area of research. Successes in the application of Artificial Neural Networks, and specifically in Deep Learning Neural Networks, has delivered a new optimism that the lack of data and limited feature sets can be overcome by using Deep Learning methods. In this poster we will present a comparison of various machine learning methods applied to several toxicological and physicochemical parameter endpoints. This abstract does not reflect U.S. EPA policy.
Deep Learning on nVidia GPUs for QSAR, QSPR and QNAR predictionsValery Tkachenko
While we have seen a tremendous growth in machine learning methods over the last two decades there is still no one fits all solution. The next era of cheminformatics and pharmaceutical research in general is focused on mining the heterogeneous big data, which is accumulating at ever growing pace, and this will likely use more sophisticated algorithms such as Deep Learning (DL). There has been increasing use of DL recently which has shown powerful advantages in learning from images and languages as well as many other areas. However the accessibly of this technique for cheminformatics is hindered as it is not available readily to non-experts. It was therefore our goal to develop a DL framework embedded into a general research data management platform (Open Science Data Repository) which can be used as an API, standalone tool or integrated in new software as an autonomous module. In this poster we will present results of comparing performance of classic machine learning methods (Naïve Bayes, logistic regression, Support Vector Machines etc.) with Deep Learning and will discuss challenges associated with Ddeep Learning Neural Networks (DNN). The DNN learning models of different complexity (up to 6 hidden layers) were built and tuned (different number of hidden units per layer, multiple activation functions, optimizers, drop out fraction, regularization parameters, and learning rate) using Keras (https://keras.io/) and Tensorflow (www.tensorflow.org) and applied to various use cases connected to prediction of physicochemical properties, ADME, toxicity and calculating properties of materials. It was also shown that using nVidia GPUs significantly accelerates calculations, although memory consumption puts some limits on performance and applicability of standard toolkits 'as is'.
Using publicly available resources to build a comprehensive knowledgebase of ...Valery Tkachenko
There is a variety of public resources on the Internet which contain information about various aspects of chemical, biological and pharmaceutical domains. The quality, maturity, hosting organizations, team sizes behind these data resources vary wildly and as a consequence content cannot be always trusted and the effort of extracting information and preparing it for reuse is repeated again and again at various levels. This problem is especially serious in applications for QSAR, QSPR and QNAR modeling. On the other hand authors of this poster believe, based on their own extensive experience building various types of chemical, analytical and biological databases for decades, that the process of building such knowledgebase can be systematically described and automated. This poster will outline the work performed on text and data-mining various public resources on the Web, data curation process and making this information publicly available through a portal and a RESTful API. We will also demonstrate how such knowledgebase can be used for real-time QSAR and QSPR predictions.
Need and benefits for structure standardization to facilitate integration and...Valery Tkachenko
There are a large number of US government databases housing diverse collections of chemical data including bioassay data (PubChem), toxicity data (CompTox Chemistry Dashboard) and environmental data (a large collection of EPA databases), to name just a few. In many cases integration between the databases, at the chemical structure level, is via alphanumeric text identifiers such as CAS Numbers, or via InChI (International Chemical Identifiers). Structure-based integration is hyper-dependent on the initial inputs providing the chemical structures to the InChI generation algorithm. To ensure optimal integration between various databases, community standards and agreement regarding standardization of chemical structures would be beneficial, not only to integration of US government databases and resources but also to the international scientific community and hosts of online databases. This presentation will discuss our progress to deliver a fully Open Source chemical standardization platform as an exemplar for the community to build on and enhance. The system utilizes the CDK (Chemistry Development Kit), RD Kit and other open source components. The resource expands on our previous work regarding the Chemical Validation and Standardization Platform and has been tested using the open data collection provided by the EPA Comptox Chemistry Dashboard.
Development and comparison of deep learning toolkit with other machine learni...Valery Tkachenko
The next era of cheminformatics and pharmaceutical research in general is focused on mining the heterogeneous big data, which is accumulating at ever growing pace, and this will likely use more sophisticated algorithms such as deep learning. There has been increasing use of deep learning which has shown powerful advantages in learning from images and languages as well as many other areas. However the accessibly of this technique for cheminformatics is hindered as it is not available readily to non-experts, it is currently not in any of the major cheminformatics tools. It is therefore our goal to develop a deep learning algorithm and toolkit which can be used as a standalone or integrated in new software being developed by us such as the Open Science Data Repository (OSDR). We will show how classic machine learning (CML) methods (Naïve Bayes, logistic regression, Support Vector Machines etc.) compares to cutting edge deep learning and talk about challenges associated with deep neural networks (DNN) learning models. The open source Scikit-learn (http://scikit-learn.org/stable/) ML python library was used for building, tuning, and validating all CML models. The DNN learning models of different complexity (up to 6 hidden layers) were built and tuned (different number of hidden units per layer, multiple activation functions, optimizers, drop out fraction, regularization parameters, and learning rate) using Keras (https://keras.io/), a deep learning library, and Tensorflow (www.tensorflow.org) as a backend. All the developed pipelines consist of stratified splitting of the input dataset into train (80%) and test (20%) datasets. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were computed for each model for ADME/Tox and other physicochemical properties. DNN learning models were found to be very good in predicting activities and can outperform most of the CML models.
Living in a world of federated knowledge challenges, principles, tools and ...Valery Tkachenko
Over years a multitude of chemical formats and approaches were created to address various aspects of handling chemical information and building databases of chemical knowledge. As a result the current state of this landscape is severely affected by the lack of well-accepted and community-recognized formats, protocols, metadata standards, validation routines and standards in handling, storing and representation, lack of open toolkits which conform to the same standards as well as the lack of platforms which allow interactive and collaborative work to solve all the above problems. While such organizations as RDA and IUPAC as well as some government agencies and institutes are concerned and trying to address the problem it is still a severe pain point. In this presentation we will talk about our experience of building a federated knowledgebase called Open Science Data Repository which supports deposition of raw and structured chemical and analytical data in various formats, runs validation and standardization protocols, is build in a highly modular way that allows using both its API and its components in a Cloud or to be deployed on premises behind firewalls and supports a variety of use cases including collaborative data curation, rich analytics and visualization, real-time machine learning, formats conversion and preparing depositions into PubChem and ChemSpider from a variety of sources and fully supports FAIR principles for research data.
Open chemistry registry and mapping platform based on open source cheminforma...Valery Tkachenko
The Open PHACTS project (openphacts.org) is a European initiative, constituting a public–private partnership to enable easier, cheaper and faster drug discovery. The project is supported by the OpenPHACTS Foundation (www.openphactsfoundation.org) and funded by contributions from several pharmaceutical companies. As part of Open PHACTS, a 'Chemical Registration Service” was created to register chemicals of interest to the project, allowing compound linkage between data sets. A key concept is the support for 'scientific lenses,' which allows hierarchical mapping of chemical entities, including supporting characteristics such as charge state, tautomerism and stereochemistry. Open PHACTS aggregated various databases, including ChEMBL, ChEBI, HMDB, DrugBank, PDB, MeSH, and WikiPathways. A new project builds on the Chemical Registration Service to establish an open chemistry registry and mapping service for general data set linkage. This expansion requires the support of multiple cheminformatics formats, the conversion and mapping of various identifiers, harmonized but configurable standardization, validation of the chemical structures, and the creation of new identifiers, to produce scientific lenses, or 'link sets'. Furthermore, these identifiers will be related to the compounds chemical names (IUPAC and trivial) and related chemical structures. This presentation will describe our ongoing work to create a fully open source, easy to install platform, which supports the ideas introduced by the Open PHACTS project and expands it with community data including, for example, the data now available from the EPA CompTox Chemistry Dashboard (comptox.epa.gov). This new platform supports chemical formats and provides for identifier conversion and cross-validation between datasets. The project is completely based on open source cheminformatics toolkits and available as a set of libraries, docker images and a web frontend based on FAIR and Open Data principles. The openness of this platform will allow for scientists to process their own datasets, and make them interoperable with other online chemical databases.
Using the structured product labeling format to index versatile chemical dataValery Tkachenko
Structured Product Labeling (SPL) is a document markup standard approved by the Health Level Seven (HL7) standards organization and adopted by the FDA as a mechanism for exchanging product and facility information. Product information provided by companies in SPL format may be accessed from the FDA Online Label Repository (labels.fda.gov) and the National Library of Medicine DailyMed web site (dailymed.nlm.nih.gov). FDA also maintains and publishes SPL Indexing Files for Pharmacologic Class, Substance, Product Concept, Biological Drug Substance, and Billing Units. Data from the Indexing Files can be linked to data in both SPL resources and external resources via chemical and non-chemical identifiers. In this talk we will present on the latest addition to SPL which allows indexing data on proteins, polymers and structurally diverse substances. We will also discuss the potential value of SPL to the integration between public chemistry databases, especially those hosted by the United States Government.
Tools and approaches for data deposition into nanomaterial databasesValery Tkachenko
Sustainable research progress in many scientific disciplines critically depends on the existence of robust specialized databases that integrate and structure all available experimental information in the respective fields. The need for such reference database is especially critical for nanoscience and nanomaterial research given the significant diversity of shapes, sizes, and properties of engineered nanomaterials and the difficulty of synthesizing engineered nanoparticles with controlled properties. The acquisition of data from public sources is inefficient, time consuming and limited in scope. Moreover, it is not clear where the resources come from to support this activity on a perpetual basis. The NIH has recently posted its intention to provide special funds toward data deposition by the experimental investigators through the ‘data sharing plan’ for each proposal. However, this points to a current weakness which is that all laboratories use different data collection approaches each of which requires interpretation by staff hosting the database. It would be far more efficient and useful if a template with key terms that could be modified to add new or important additional data or parameters for each investigator. We will discuss tools and approaches to facilitate collection and direct deposition of experimental data into Nanomaterial Registry (https://www.nanomaterialregistry.org/) - a versatile semantically enriched templates-based platform for registering diverse data pertaining to nanomaterials research.
Chemistry Validation and Standardization Platform v2.0Valery Tkachenko
In recent years there has been explosive growth in the number of public chemical databases available online, a number of these containing 10s of millions of chemical structures. Examples include PubChem, ChemSpider and ChEMBL and users of these databases have become increasingly aware of the issue of data quality associated with these public resources. Seamless integration and mapping between databases, even for some common chemicals, is challenged by differing approaches to chemical standardization prior to registration into a database. The lack of standards in representing and handling chemical information certainly contributes to aspects of this problem. The Chemistry Validation and Standardization Platform (CVSP), originally developed to support the European Innovative Medicines Initiative project known as OpenPHACTS, was developed with the intention of providing an open platform for processing and standardizing chemical compounds. The system has been used to process millions of chemical compounds for dissemination through public websites and, unlike other validation and standardization systems, the system provides support for both standard and custom rulesets. We will provide an overview of CVSP 2.0, the next generation of the platform extending support to new cheminformatics toolkits and additional capabilities such as collaborative rules authoring.
Open Science Data Repository - the platform for materials researchValery Tkachenko
Over the last few years we have seen a tremendous growth in various data repositories pushed and supported by funding bodies and various data preservation initiatives. As a result we have now a variety of scientific resources, combined into a broad network and indexed through the directories like BioSharing and re3data. Such network, while growing quickly, is still in early days of adopting semantic web standards and does not yet support deep data indexing and discoverability, leave alone that mechanisms of intellectual properties protection are as simple as making data public or private at best. The lack of standards and well defined models to describe a scientific information structure even further inhibits free information flow which is essential for scientific discovery. One of the most affected areas is not surprisingly materials sciences where due to the inherent complexity of the field of study the situation is even more severe. In this talk we present a chemistry information platform designed to support a variety of data formats along with metadata, sophisticated ways of collaboration and secure data exchanges. We will discuss challenges that we have faced developing such platform as well as solutions that we have came with.
Opportunities in chemical structure standardizationValery Tkachenko
This talk was given at EBI's Wellcome Trust Genome Campus and is dedicated to outlining problems with chemical information standardization and various efforts to tackle this problem.
In last few years the number and the size of chemical databases has been steadily increasing, as has the complexity of information residing in those databases creating truly multidimensional chemical spaces. Yet the most common user interface approach still remains based on search-and-browse workflow thus essentially preventing a proper navigation through such databases and hiding data patterns which may belong to other dimensions. As we at the Royal Society of Chemistry are building a chemical database service it is potentially useful to be able to visualize large chemical spaces, ranging in size from tens of thousands to tens of millions of compounds. Dimensionality reduction techniques such as PCA have been used to produce two-dimensional displays of large chemical spaces, via the production of scatterplots. Standard chart-plotting libraries allow interactive scatterplots to be produced, but do not scale well to large numbers of data points. Our new visualisation tool, OMPOL, is a browser-based tool for displaying and interacting with these data sets, allowing people to smoothly and responsively pan and zoom these plots, view the names and structures associated with the data points, select regions of chemical space and find typical and atypical members of those regions.
Building linked data large-scale chemistry platform - challenges, lessons and...Valery Tkachenko
Chemical databases have been around for decades, but in recent years we have observed a qualitative change from rather small in-house built proprietary databases to large-scale, open and increasingly complex chemistry knowledgebases. This tectonic shift has imposed new requirements for database design and system architecture as well as the implementation of completely new components and workflows which did not exist in chemical databases before. Probably the most profound change is being caused by the linked nature of modern resources - individual databases are becoming nodes and hubs of a huge and truly distributed web of knowledge. This change has important aspects such as data and format standards, interoperability, provenance, security, quality control and metainformation standards.
ChemSpider at the Royal Society of Chemistry was first public chemical database which incorporated rigorous quality control by introducing both community curation and automated quality checks at the scale of tens of millions of records. Yet we have come to realize that this approach may now be incomplete in a quickly changing world of linked data. In this presentation we will talk about challenges associated with building modern public and private chemical databases as well as lessons that we have learned from our past and present experience. We will also talk about solutions for some common problems.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Cancer cell metabolism: special Reference to Lactate Pathway
Not just another reaction database
1. Not just another Reaction Database
Aileen Day1, Valery Tkachenko1, Alexey Pshenichnov1, Leah McEwen2,
Simon Coles3, Richard Whitby3
1Data Science, Royal Society of Chemistry
2Physical Sciences Library, Cornell University
3Department of Chemistry, University of Southampton
6. The RSC data repository is under development, and
is intended to contain chemical data which supports
its publications.
A first version has been written which captures
compounds, data sources and properties domains.
Reactions are next…
RSC data repository
Compounds PropertiesData sources Reactions
7. • There are a lot of reactions databases already – many established with
many reactions
• This reactions database aims to capture reactions:
• in sufficient detail for someone else to reproduce
• in analogous ways to those captured in Electronic Lab Notebook
• with fully recorded processes, parameters and equipment in S88
process recipe [1] style
• raw characterization data linked to products
• which gave low yields or unintended products
• multistep reactions
• to fully record all reaction products (not just the target product)
• Guided by the aims of Dial-a-Molecule
RSC data repository - reactions
DetailScope
11. • Reaction 1: Example of reaction text-mined
from RSC archive by NextMove with S88-
style procedure
• Reaction 2: Example From Will Dichtel’s
research group via Leah McEwen (ELN-style
reaction)
Reaction examples
13. Reaction 1: NextMove reaction text-
mined from RSC archive – cml output
<?xml version="1.0" encoding="UTF-8"?>
<reactionList xmlns="http://www.xml-cml.org/schema" xmlns:cmlDict="http://www.xml-cml.org/dictionary/cml/" xmlns:nameDict="http://www.xml-cml.org/dictionary/cml/name/"
xmlns:unit="http://www.xml-cml.org/unit/" xmlns:cml="http://www.xml-cml.org/schema" xmlns:dl="http://bitbucket.org/dan2097">
<reaction>
<dl:source>
<dl:documentId>c3ra45871g</dl:documentId>
<dl:paragraphText>Diisobutylaluminium hydride (1.1 M in cyclohexane, 2.93 mL, 3.23 mmol) was added dropwise to the solution of 9 (500 mg, 1.29 mmol) and dichloromethane (20 mL) at −78 °C. The
reaction mixture was stirred at −78 °C for another 2 h, warmed up to rt, quenched with methanol (3 mL) and citric acid(aq) (w/w, 10%, 5 mL), concentrated. The residue was added with water (10 mL) and
extracted with dichloromethane (12 mL × 3). The organic layers were combined, dried over Na2SO4, filtered and concentrated. The crude product was further purified by column chromatography (SiO2,
EtOAc–hexanes, 1 : 7; Rf 0.33) to give 10 (308 mg, 1.02 mmol, 79%) as a colourless liquid. [α]D20 −24.2 (c 1.1, CHCl3); 1H NMR (CDCl3, 300 MHz) δ 0.04 (s, 3H), 0.07 (s, 3H), 0.85 (s, 9H), 1.34 (s, 3H), 1.44 (s,
3H), 2.16 (br, 1H), 3.68–3.81 (m, 3H), 4.16 (t, J = 13.8 Hz, J = 13.8 Hz, 1H), 4.59 (t, J = 6.6 Hz, J = 6.6 Hz, 1H), 5.22 (d, J = 10.7 Hz, 1H), 5.34 (d, J = 17.1 Hz, 1H), 5.90 (ddd, J = 7.2 Hz, J = 10.2 Hz, J = 17.2 Hz, 1H);
13C NMR (CDCl3, 75 MHz) δ 134.1, 118.4, 108.5, 79.5, 78.8, 70.8, 65.0, 27.8, 25.9, 25.4, 18.1, −3.7, −4.4. HRMS (ESI) calcd for [M + Na]+ (C15H30O4SiNa) 325.1811, found 325.1807.</dl:paragraphText>
</dl:source>
<dl:reactionSmiles>[H-
].C([Al+]CC(C)C)C(C)C.C([O:17][CH2:18][C@@H:19]([O:29][Si:30]([C:33]([CH3:36])([CH3:35])[CH3:34])([CH3:32])[CH3:31])[C@@H:20]1[C@H:24]([CH:25]=[CH2:26])[O:23][C:22]([CH3:28])([CH3:27])[O:21]1)
(=O)C(C)(C)C>ClCCl>[C:33]([Si:30]([CH3:32])([CH3:31])[O:29][C@@H:19]([C@@H:20]1[C@H:24]([CH:25]=[CH2:26])[O:23][C:22]([CH3:28])([CH3:27])[O:21]1)[CH2:18][OH:17])([CH3:36])([CH3:35])[CH
3:34] |f:0.1|</dl:reactionSmiles>
<productList>
<product role="product">
<molecule id="m0">
<name dictRef="nameDict:unknown">10</name>
<dl:nameResolved>(R)-2-((tert-Butyldimethylsilyl)oxy)-2-((4S,5S)-2,2-dimethyl-5-vinyl-1,3-dioxolan-4-yl)ethanol</dl:nameResolved>
</molecule>
<amount dl:propertyType="AMOUNT" dl:normalizedValue="0.00102">1.02 mmol</amount>
<amount dl:propertyType="MASS" dl:normalizedValue="0.308">308 mg</amount>
<amount dl:propertyType="PERCENTYIELD" dl:normalizedValue="79">79%</amount>
<amount dl:propertyType="CALCULATEDPERCENTYIELD" dl:normalizedValue="79.1" units="unit:percentYield">79.1</amount>
<identifier dictRef="cml:smiles" value="C(C)(C)(C)[Si](O[C@H](CO)[C@H]1OC(O[C@H]1C=C)(C)C)(C)C"/>
<identifier dictRef="cml:inchi" value="InChI=1S/C15H30O4Si/c1-9-11-13(18-15(5,6)17-11)12(10-16)19-20(7,8)14(2,3)4/h9,11-13,16H,1,10H2,2-8H3/t11-,12+,13-/m0/s1"/>
<dl:entityType>definiteReference</dl:entityType>
<dl:appearance>colourless</dl:appearance>
14. Reactions properties
[1] https://github.com/rsc-ontologies/rxno
[2] Carey, Laffan, Thomson and Williams hierarchy: DOI: 10.1039/B602413K
Reactions Substances Procedures Equipment
Compounds
Mixtures
Solutions
Samples
Reaction runs Steps
ParametersReaction is defined by:
• Reaction Smiles from textmining output
• NextMove’s NameRXN program
categorises reaction by:
• Named Reaction ontology ID and
name [1]
• Reaction Class and name [2]
15. Reaction 1: Reaction
Reactions
• Reaction SMILES: [H-
].C([Al+]CC(C)C)C(C)C.C([O:17][CH2:18][C@@H:19]([O:29][Si:30]([C:33]([CH3:36])([CH3:35])[CH
3:34])([CH3:32])[CH3:31])[C@@H:20]1[C@H:24]([CH:25]=[CH2:26])[O:23][C:22]([CH3:28])([CH
3:27])[O:21]1)(=O)C(C)(C)C>ClCCl>[C:33]([Si:30]([CH3:32])([CH3:31])[O:29][C@@H:19]([C
@@H:20]1[C@H:24]([CH:25]=[CH2:26])[O:23][C:22]([CH3:28])([CH3:27])[O:21]1)[CH2:18][OH:
17])([CH3:36])([CH3:35])[CH3:34] |f:0.1|
• ReactionClass: “9.7 Other functional group interconversion”
• Other Named Reaction: “9.7.61 Ester hydrolysis”
From Nextmove’s namerxn reaction output (software
source should be linked from Properties database)
As well as reaction SMILES we can store Reaction RXN, RD and ChemDraw files.
16. Reaction 1: Reaction reference
Reference
• URL: http://dx.doi.org/10.1039/c3ra45871g
• Title: "Diastereoselective vinylalumination for the synthesis of pericosine A, B and C"
• Description: Reaction text-mined by NextMove from RSC article with DOI:
10.1039/c3ra45871g
• Authors: Long-Shiang Li; Duen-Ren Hou
• Publication Date: 31/10/2013
• DOI: 10.1039/c3ra45871g
• Journal: RSC Advances
• Publication Type: Journal Article
Reference Details
• External Identifier: c3ra45871g: product 10
• Paragraph Text: Diisobutylaluminium hydride (1.1 M in cyclohexane, 2.93 mL,
3.23 mmol) was added dropwise …
17. RSC data repository – reaction components
Reactions Substances Procedures Equipment
Compounds
Mixtures
Solutions
Samples
Reaction runs Steps
Parameters
Reaction components define each reaction
and each component is:
• Defined as a
substance/compound/solution/mixture
• Assigned a reaction role is stored which
can take values Reactant/ Product/
Solvent/ Catalyst/ Intermediate/
ChiralAuxiliary
Text-mining identifies all compounds and solutions (indicated by
molarity) that play a role in each reaction and returns smiles, InChI,
reaction role, and amounts of each.
18. Reaction 1: compounds and solutions
Diisobutylaluminium hydride (1.1 M in cyclohexane,
2.93 mL, 3.23 mmol) was added dropwise to the
solution of 9 (500 mg, 1.29 mmol) and
dichloromethane (20 mL) at −78 °C. The reaction
mixture was stirred at −78 °C for another 2 h, warmed
up to rt, quenched with methanol (3 mL) and citric
acid (aq) (w/w, 10%, 5 mL), concentrated. The residue
was added with water (10 mL) and extracted with
dichloromethane (12 mL × 3). The organic layers were
combined, dried over Na2SO4, filtered and
concentrated. The crude product was further purified
by column chromatography (SiO2, EtOAc–hexanes,
1 : 7; Rf 0.33) to give 10 (308 mg, 1.02 mmol, 79%) as
a colourless liquid.
Solutions
• Diisobutylaluminium hydride
Compounds
• 9
• dichloromethane
• methanol
• citric acid
• water
• Dichloromethane
• Na2SO4
• 10
Ignored for now (only the name was
extracted in this pass) – in time
“Substances”
• SiO2
• EtOAc–hexanes
Reaction components: reactant , solvent, product
Other compound/substance used in procedure
20. Reaction 1: Reaction rendering
Reaction
Solution: Diisobutylaluminium hydride
• Components:
When you click on it
• Solution Role: Solute; Molarity:
1.1M; Compound:
Diisobutylaluminium(1+) hydride:
• Solution Role: Solvent;
Compound: cyclohexane
21. RSC data repository – reaction runs
Reactions Substances Procedures Equipment
Compounds
Mixtures
Solutions
Samples
Reaction runs Steps
ParametersWhile the reaction information defines the
overall reaction, the details about each
specific instance of performing the reaction
are stored in reaction runs:
• stoichiometry table of each component
• labels of components
• amounts of components
• links to specific samples and sources
• results and yields of products.
22. Reaction 1: Reaction Run
Reaction Run
• Label: Preparation of lithium acetylide (phenylethynyl)lithium; Experiment Stage: Executed
• Stoichiometry Table Rows
Label Reaction
Component
Volume
(mL)
Mass
(mg)
Moles
(mMol)
Percentage
Yield (%)
Substance
State
Diisobutylalumini
um hydride
Reactant: 2.93 3.23 Liquid
9 Reactant 500 1.29 Solid
dichloromethane Solvent 20 Liquid
10 Product 308 1.02 79 Solid
23. RSC data repository – procedure
Reactions Substances Procedures Equipment
Compounds
Mixtures
Solutions
Samples
Reaction runs Steps
Parameters
For reactions to be fully reproducible and
queryable they are captured in a way
analagous to S88 process recipes [1]:
1. Break process down into a series of steps
(actions)
2. Define parameters at any level (for whole
experiment or for particular action)
3. Define equipment at any level (for whole
experiment or for particular action)
[1] https://en.wikipedia.org/wiki/ISA-88
24. S88-style procedures
Type of actions which can be assigned to procedure
steps
Action Types
Add Synthesize Wait Degass
Yield Wash Unknown Irradiate
Stir Extract Precipitate Mill
Remove Filter Partition Sample
Heat Concentrate Quench Reflux
Dry Cool Apparatus Action Transfer
Purify Dissolve Recover
25. S88-style procedures
Parameters that can be assigned to actions or
experiments
rate
speed pH
time
pressure
particle size
volume
weight
quantity
temperaturesample ID
Substance
Parameters Other Parameters
Can be time
dependent
26. Reaction 1: procedure steps
Diisobutylaluminium hydride (1.1 M in cyclohexane,
2.93 mL, 3.23 mmol) was added dropwise to the
solution of 9 (500 mg, 1.29 mmol) and
dichloromethane (20 mL) at −78 °C. The reaction
mixture was stirred at −78 °C for another 2 h,
warmed up to rt, quenched with methanol (3 mL)
and citric acid (aq) (w/w, 10%, 5 mL), concentrated.
The residue was added with water (10 mL) and
extracted with dichloromethane (12 mL × 3). The
organic layers were combined, dried over Na2SO4,
filtered and concentrated. The crude product was
further purified by column chromatography (SiO2,
EtOAc–hexanes, 1 : 7; Rf 0.33) to give 10 (308 mg,
1.02 mmol, 79%) as a colourless liquid.
Text mining breaks down procedure summary into steps:
<dl:reactionActionList/dl:reactionActions> dl:phraseTexts
• action="Add“: Diisobutylaluminium hydride (1.1 M in
cyclohexane, 2.93 mL, 3.23 mmol) was added dropwise to the
solution of 9 (500 mg, 1.29 mmol) and dichloromethane (20
mL) at −78 °C
• action=" Stir“: The reaction mixture was stirred at −78 °C for
another 2 h
• action="Heat“: warmed up to rt
• action="Quench“: quenched with methanol (3 mL) and citric
acid(aq) (w/w, 10%, 5 mL)
• action="Concentrate“: concentrated
• action="Add“: The residue was added with water (10 mL)
• action="Extract“: extracted with dichloromethane (12 mL × 3)
• action="Dry“: dried over Na2SO4
• action="Filter“: filtered
• action="Concentrate“: concentrated
• action="Purify“: The crude product was further purified by
column chromatography (SiO2, EtOAc–hexanes, 1 : 7; Rf 0.33)
• action="Yield“: to give 10 (308 mg, 1.02 mmol, 79%) as a
colourless liquid
27. Reaction 1: Example Reaction Step 1
Procedure Step
• Ordinal:1; Title: Add; Experiment Stage: Executed
• Description: Diisobutylaluminium hydride (1.1 M in cyclohexane, 2.93 mL, 3.23 mmol) was
added dropwise to the solution of 9 (500 mg, 1.29 mmol) and dichloromethane (20 mL) at −78
°C
• Type: “Add”
• Parameters:
• Substance: Stoichiometry Table Row for Diisobutylaluminium hydride
• Substance: Stoichiometry Table Row for 9
• Substance: Stoichiometry Table Rowfor dichloromethane
• Temperature:
• Value: -78C
<dl:reactionAction action="Add">
<dl:phraseText>Diisobutylaluminium hydride (1.1 M in cyclohexane, 2.93 mL, 3.23 mmol) was added dropwise to the
solution of 9 (500 mg, 1.29 mmol) and dichloromethane (20 mL) at −78 °C</dl:phraseText>
<dl:chemical ref="m1"/> <dl:chemical ref="m2"/><dl:chemical ref="m3"/>
<dl:parameter propertyType="Temperature" normalizedValue="-78">-78 °C.</dl:parameter>
</dl:reactionAction>
Underlined values are retrieved
from elsewhere in the repository
(so that if e.g. amounts are
updated, changes can be made in
one place and be picked up
28. Reaction 1: Example Reaction Step 2<dl:reactionAction action="Stir">
<dl:phraseText>The reaction mixture was stirred at −78 °C for another 2 h</dl:phraseText>
<dl:parameter propertyType="Time" normalizedValue="7200">2 h</dl:parameter>
<dl:parameter propertyType="Temperature" normalizedValue="-78">-78 °C</dl:parameter>
</dl:reactionAction>
Procedure Step
• Ordinal:2; Title: Stir; Experiment Stage: Executed
• Description: The reaction mixture was stirred at −78 °C for another 2 h
• Type: “Stir”
• Parameters:
• Temperature:
• Value: -78C
• Time: 2 hours
30. Reaction 2: ELN-style reaction
Example reaction from Cornell (Will Dichtel’s research group, via Leah
McEwen):
• Multiple “runs” of a reaction are performed, with different amounts, and
under different conditions
• Results, observations and product characterisations are stored for each
• This allows the run which gives rise to the best yield to be identified
• Currently the experient files are stored in a number of files (see below), but
this information is suitable to be stored in an Electronic Lab Notebook:
– SJH-01-227_Enotebook.docx (“notebook” which shows the details of a particular run
of a reaction – stoichiometry table (embedded Excel spreadsheet which does
calculations), actual quantities, notes of conditions and results and TLC images
embedded
– WeeklyReport_5_01_2014.docx (logs all runs of all reactions done during a particular
week – grouped by reaction, with reaction schema and observations noted)
– spectra files
31. SJH-01-227_Enotebook.docx
SJH-01-227_Enotebook.docx
Actual quantities
SJH-01-223 0.1009 g
Benzaldehyde 0.0554 g
Cu(OTf)2 0.0055 g
TFA 0.030 mL
EQ FW MMOL g d mL Reagent
1 756.95 0.132 0.100 SJH-01-223
6 206.24 0.793 0.163 Benzaldehyde
0.1 361.67 0.013 0.005 Cu(Otf)2
3 114.02 0.396 0.045 1.49 0.030 TFA
Conc in line 1 (M): 0.100 1.321 DCE
1 1063.36 0.132 0.140 Theoretical Yield
SJH-01-227 11/4/2014
SJH-01-223 and Cu(OTf)2 was transferred to a 5mL RBF with
a reflux condenser with a schlenk adaptor and put under a N2
environment. The benzaldehyde was dissolved in
dichloroethane and this solution was added via syringe to the
RBF reaction flask. The flask was then placed in a 100°C oil
bath and TFA was added via Hamilton microsyringe. The
reaction stirred or 30 min.
When complete, the reaction was washed with sat.
NaHCO3(aq) and extracted three times with DCM. The
organic fractions were collected and dried with MgSO4,
filtered and solvent was removed under vacuum. The
product was purified on SiO2 column chromatography (3:7
DCM:Hexanes).
The product was isolated as a light yellow solid
0.0963 g (68% Yield).
Reaction run -
stoichiometry
table
Procedure, parameters,
substance parameters,
equipment
Procedure-
results
Reaction database
Procedure - results
32. WeeklyReport_5_01_2014.docx
Date NB Page Type Comments
12/7/2013 SJH-01-211 0.015g Did a prep plate purification an isolated ~0.002g from my top band.
HNMR is tricky, not sure if I made it. Did not see anything on MALDI
(graphite, no matrix, or 2,5 dihydroxy benzoic acid). GCMS shows a
peak, retention time 13.01 min with m/z = 202. I don’t know what this
mass equates to.
1/11/2014 SJH-01-227 0.100 Isolated 0.0963g. ASAP does not show significant surface area (20ish)
Flourescence does not change much but UV absorption does blue shift
after benzannulation.
2/12/2014 SJH-01-227 2D high temp NMR is much more simplified than previous 2D NMRs. I
haven’t yet gotten a chance to look through them and process the
spectra. The 13C looks significantly simplified as well with 24 signals.
Ivan has a partial assignment finished and we think we’ve figured out
where the proton on the central benzene ring is. For a more complete
assignment he said he or Tony would help me set up a band specific
HMBC and HSQC to help solve some of the ambiguities in the NMR. He
was using the low temp NMR to solve.
2/26/2014 Set up a band specific HMBC and HSQC for this with the help of Tony
last night. The HMBC does not have good sensitivity for some reason.
Tony is going to talk to Ivan about this and we should be able to get it
next week.
3/26/2014 SJH-01-298 0.150 Was going to run this reaction last night but I opened the flask under
vacuum instead of nitrogen and SM got sucked up into the hose. I
extracted out the compound best I could. I’ll need to repurify but I
should be able to do this reaction today.
3/26/2014 SJH-01-298 0.1417g Isolated 0.1235g of final product. 62%Y. Confirmed by MALDI and NMR
Working on Structural assignments with Ivan.
Experiment observations mostly – stored
in Procedure results
33. • Files that would probably go into spectra bucket of data repository:
– SJH-01-227.jdx or SJH-01-227_jcamp.jdx (IR spectrum files - same content)
– SJH-01-227_22-145C.jdx (1H NMR spectrum)
– SJH-01-227-RT-2D.jdx (2D 1H NMR spectrum)
• Other files which might be processed (to extract e.g. store peak assignment values into the data repository so that they can be exported):
– SJH-01-227_DCM_rsw.rsw or SJH-01-227_DCM_rtf.rtf (UV-VIS-NIR peaks in text file – nearly the same as each other)
• Other files (we think duplicates of the above):
– SJH-01-227.spa (binary file)
– SJH-01-227_csv.csv (text, but with no headers)
– SJH-01-227_grams.spc (binary file)
– SJH-01-227_mattson.ras (binary file)
– SJH-01-227_nicolet.nic (binary file)
– SJH-01-227_pcir.ird (binary file)
– SJH-01-227_spa.spa (binary file)
– SJH-01-227_spectacle.irs (binary file)
– SJH-01-227_tiff.tiff and SJH-01-227_wmf.wmf (image files of the same spectrum)
– SJH-01-227_DCM_baseline.csw (UV-VIS-NIR, binary file)
– SJH-01-227_DCM_bsw.bsw (UV-VIS-NIR spectrum, binary file)
– SJH-01-227_DCM_csv.csv (might be able to do something with this – UV?)
– SJH-01-227_DCM_dsw.dsw (UV-VIS-NIR spectrum, binary file)
– SJH-01-227_DCM_grams.spc (UV-VIS-NIR spectrum, binary file)
– SJH-01-227_DCM_gsw.gsw (UV-VIS-NIR spectrum, binary file)
– SJH-01-227_DCM_msw.msw (UV-VIS-NIR spectrum, binary file)
Other spectra files Spectra database ultimately
(but Procedure Results Files
for now)
Procedure
Results files
Use this as an interim example
34. ESI docx example – synthetic procedure
Synthesis of 17: 16 (0.101 g, 0.132 mmol) and Cu(OTf)2 (0.006 g, 0.01 mmol) were added to a round-bottom
flask under a N2 atmosphere. In a separate vial, 2 (0.155 g, 0.753 mmol) was dissolved in C2H4Cl2 (1.3 mL) and
transferred to the reaction flask. CF3CO2H (0.030 mL, 3 equiv) was added to the reaction mixture, which was
refluxed at 100 °C for 1 h. The reaction mixture was washed with saturated NaHCO3 (15 mL) and extracted with
C2H4Cl2 (3 x 5 mL). The organic fractions were collected, dried (MgSO4), and filtered to give a dark red solution.
The solvent was removed, and the product was purified by column chromatography (SiO2, 30:70 CH2Cl2 :
hexane) to yield 17 as a pale yellow powder (0.096 g, 68% yield). 17: 1H NMR (500 MHz, CDCl3): δ 8.15 (d, 2H),
8.13 (s, 1H,), 7.98 (s, 1H), 7.95 (s, 2H), 7.92 (d,2H), 7.88 (d, 1H), 7.87 (d, 1H), 7.84 (d, 1H), 7.80 (s, 1H), 7.69 (t,
2H), 7.64 (d, 2H), 7.57 (t, 2H), 7.56 (s, 2H), 7.54 (s, 2H), 7.54 (d, 2H), 7.45 (t, 1H), 7.44 (t, 2H), 7.40 (t, 2H), 7.39 (t,
1H), 7.38 (t, 1H), 7.34 (t, 1H), 6.88 (t, 4H), 6.88 (t, 2H), 6.80 (s, 2H), 6.77 (d, 4H), 6.70 (d, 1H), 6.50 (t, 1H), 6.39
(d, 2H), 6.24 (t, 2H), 6.22 (s, 1H), 6.11 (s, 2H), 6.04 (s, 1H). 13C NMR (125 MHz, CDCl3) δ 141.47, 141.10, 140.85,
140.42, 140.32, 140.20, 140.10, 139.60, 139.45, 139.37, 139.16, 139.03, 138.72, 138.28, 138.07, 133.28, 133.04,
132.96, 132.90, 132.64, 132.37, 131.60, 131.41, 131.19, 131.17, 130.72, 130.48, 130.28, 129.87, 129.85, 129.57,
129.30, 129.16, 129.11, 128.35, 128.21, 128.08, 128.04, 127.86, 127.72, 127.47, 126.85, 126.65, 126.50, 126.32,
126.25, 126.17, 126.08, 125.98, 125.84. IR (solid, ATR) 3051, 2925, 2131, 1947, 1590, 1488, 1444, 1415, 1318,
1274, 1180, 1133, 1074, 1018, 950, 882, 870, 809, 771, 743, 720, 697 cm-1. HRMS (DART) calcd for [C84H56
+]
1064.4376, found 1064.4348.
Reaction runs database - stoichiometry table, reaction results and procedure – S88
38. S88 process standard approach
Process
Process
Stage
Process
Stage
Process
Stage
Process
Operation
Process
Actions
Experiment Synthesis stage Preparation / Reaction
/ Work up / Isolation
Heat / Cool /
Dose / Stir etc.
S88 allows procedure steps (process actions) to be grouped
into “process operations”:
We allow “Procedure Steps” to be nested and have seeded the following
procedure step types to assign to procedure steps for these parent operations:
S88 process operation/Procedure. StepTypes.Title
Preparation
Reaction
S88 process operation/Procedure. StepTypes.Title
Work up
Isolation
39. Reaction 2: Planned procedure
Procedure
• Title: Reaction SJH-01-227 dated 2/12/2014; Failed Reaction: false; Experiment Stage: Planned; Link to ReactionRun
• Procedure Steps:
Ordinal Parent Title Description ParameterSubstances Parameter Equipment
1 Reaction
2 Reaction Add Add SJH-01-223 (0.1 g, 0.132 mmol)
to a 5 mL round bottom flask with a
reflux condenser with a schlenk
adaptor
• SJH-01-223
stoichiometry table row
• round bottom
flask
• Volume=5mL
• Type=Apparatus
• reflux condenser
• schlenk adaptor
3 Reaction Add Add Cu(OTf)2 (0.005 g, 0.013 mmol)
and put under a N2 environment
• Cu(OTf)2 stoichiometry
table row
• N2 environment
4 Reaction Dissolve Dissolve the benzaldehyde (0.163 g,
0.791 mmol) in DCE (1.3 mL).in a
vial
• Benzaldehyde
stoichiometry table row
• DCE stoichiometry table
• vial
5 Reaction Transfer Transfer this solution via syringe to
the reaction round bottom flask
• syringe
• round bottom
flask
40. If there are differences between the planned and executed reaction or
procedure then both versions of the following can be stored and
flagged as having an ExperimentStage field as Planned or Executed:
• Reaction run
• All corresponding stoichiometry table rows
• Procedure and for each
• All corresponding Procedure Steps and ParameterValues and
ParameterTimes
• Results and requested user inputs can be recorded and linked the
relevant procedure or step of the Executed Procedure
Reaction/Procedure Planned and
Executed Experiment Stage
41. Reaction 2: Reaction run (Executed and Planned)
Reaction
• Reaction Run: Reaction SJH-01-227 dated 2/12/2014; FailedReaction: false; Experiment Stage: Planned
• Stoichiometry Table
Reaction
• Reaction Run: Reaction SJH-01-227 dated 2/12/2014; FailedReaction: false; Experiment Stage: Executed; Link to Planned
reaction run
• Stoichiometry Table
By default, the executed version is shown, but the
planned version can be accessed via clicking on a link
Links to actual amounts of reactants/reagents used
Links to planned amounts of reactants/reagents used
42. Label Reaction Component Actual Amounts (Planned values) State Comments
SJH-01-223 Role: Limiting Reactant
Compound
Molecular Mass: 756.95
Mass: 0.1009 g
Moles: 0.133 mMol
Equivalence: 1
Solid
benzaldehyde Role: Reactant
Compound
Molecular Mass: 206.24
Mass: 0.0554 g
Moles: 0.269 mMol
Equivalence: 2.02
Solid
Cu(OTf)2 Role: Reactant
Compound
Molecular Mass: 361.67
Mass: 0.0055 g
Moles: 0.015 mMoles
Equivalence: 0.11
Solid
DCE Role: Solvent
Compound
Volume: 1.321 mL Liquid Concentration in
line 1: 0.1 M
TFA Role: Solvent
Compound
Molecular Mass: 114.02
Density: 1.49 g/ml
Volume: 0.030 mL
Mass: 0.045 g
Moles: 0.396 mMol
Equivalence: 2.97
Liquid
SJH_01_227 Role: Product
Compound
Molecular Mass: 1063.36
Mass: 0.0963 g; Moles: 0.0906 mMol
Equivalence: 0.679 (planned: 1)
Yield: 67.9%
Solid
Click to see added sample information (see next slide)
Label Reaction Component Planned Amounts State Comments
SJH-01-223 Role: Limiting Reactant
Compound
Molecular Mass: 756.95
Equivalence: 1
Moles: 0.132 mMol
Mass: 0.1 g
Solid
benzaldehyde Role: Reactant
Compound
Molecular Mass: 206.24
Equivalence: 6
Moles: 0.293 mMol
Mass: 0.163 g
Solid
Cu(OTf)2 Role: Reactant
Compound
Molecular Mass: 361.67
Equivalence: 0.1
Moles: 0.013 mMoles
Mass: 0.005 g
Solid
DCE Role: Solvent
Compound
Volume: 1.321 mL Liquid Concentration i
line 1: 0.1 M
TFA Role: Solvent
Compound
Molecular Mass: 114.02
Density: 1.49 g/ml
Equivalence: 3
Moles: 0.396 mMol
Mass: 0.045 g
Volume: 0.030 mL
Liquid
SJH_01_227 Role: Product
Compound
Molecular Mass: 1063.36
Equivalence: 1
Moles: 0.132 mMol
Mass: 0.140 g
Solid
Reaction 2: Stroichiometry table (Executed and Planned)
44. Reaction 2: Procedure (planned and executed values)
Procedure
• Title: Reaction SJH-01-227 dated 2/12/2014; FailedReaction: false; Experiment Stage: Planned; Link to ReactionRun
• Procedure Steps
Procedure
• Title: Reaction SJH-01-227 dated 2/12/2014; FailedReaction: false; Experiment Stage: Executed; Link to Planned Procedure;
Link to ReactionRun
• Procedure Steps
Links to planned ReactionRun and Procedure Steps
Links to executed ReactionRun and Procedure Steps
45. Ordinal Parent Title Description ParameterSubstances Parameter Equipment
1 Reaction
2 Reaction Add Add SJH-01-223 (0.1 g, 0.132 mmol)
to a 5 mL round bottom flask with a
reflux condenser with a schlenk
adaptor
• SJH-01-223
stoichiometry table row
• round bottom
flask
• Volume=5mL
• Type=Apparatus
• reflux condenser
• schlenk adaptor
3 Reaction Add Add Cu(OTf)2 (0.005 g, 0.013 mmol)
and put under a N2 environment
• Cu(OTf)2 stoichiometry
table row
• N2 environment
4 Reaction Dissolve Dissolve the benzaldehyde (0.163 g,
0.791 mmol) in DCE (1.3 mL).in a
vial
• Benzaldehyde
stoichiometry table row
• DCE stoichiometry table
• vial
5 Reaction Transfer Transfer this solution via syringe to
the reaction round bottom flask
• syringe
• round bottom
flask
Reaction 2: Procedure Steps (Executed version)
Ordinal Parent Title Executed Description ParameterSubstances Parameter Equipment
1 Reaction
2 Reaction Add Add SJH-01-223 (0.101 g, 0.133
mmol) to a 5 mL round bottom flask
with a reflux condenser with a
schlenk adaptor
• SJH-01-223
stoichiometry table row
• round bottom
flask
• Volume=5mL
• Type=Apparatus
• reflux condenser
• schlenk adaptor
3 Reaction Add Add Cu(OTf)2 (0.006 g, 0.015 mmol)
and put under a N2 environment
• Cu(OTf)2 stoichiometry
table row
• N2 environment
4 Reaction Dissolve Dissolve the benzaldehyde (0.155 g,
0.790 mmol) in DCE (1.3 mL).in a
vial
• Benzaldehyde
stoichiometry table row
• DCE stoichiometry table
• vial
5 Reaction Transfer Transfer this solution via syringe to
the reaction round bottom flask
• syringe
• round bottom
flask
All values that are retrieved from stoichiometry table
rows are automatically updated with Executed rather
than Planned values
46. • We have shown how this reactions database captures reactions:
• in sufficient detail for someone else to reproduce
• in analogous ways to those captured in Electronic Lab
Notebook
• with fully recorded processes, parameters and equipment in
S88 process recipe [1] style
• raw characterization data linked to products
• which gave low yields or unintended products
• multistep reactions
• to fully record all reaction products (not just the target product)
Conclusions
47. Because of all this being captured and linked…
Reactions Substances Procedures Equipment
Compounds
Mixtures
Solutions
Samples
Reaction runs Steps
Parameters
48. • We have shown 2 examples:
• Reaction 1: Example of reaction text-mined from
RSC archive by NextMove with S88-style procedure
• there are 31,000 more of these to be validated and
imported
• Reaction 2: Example From Will Dichtel’s research
group via Leah McEwen (ELN-style reaction)
• Consider pipeline for population direct from ELNs
• Develop reactions user interface, API, and
import/validation platform
Future work