Antitubercular activity of 5-nitrofuran-2-yl Deriva
tives series were subjected to Quantitative Struc
ture
Activity Relationship (QSAR) Analysis with an effo
rt to derive and understand a correlation between t
he
biological activity as response variable and differ
ent molecular descriptors as independent variables.
QSAR models are built using 40 molecular descriptor
dataset. Different statistical regression express
ions
were got using Partial Least Squares (PLS) ,Multip
le Linear Regression (MLR) and Principal Component
Regression (PCR) techniques. The among these techni
que, Partial Least Square Regression (PLS)
technique has shown very promising result as compar
ed to MLR technique A QSAR model was build by a
training set of 30 molecules with correlation coe
fficient (
) of 0.8484 , significant cross validated
correlation coefficient (
) is 0.0939,
is 48.5187,
for external test set (
_
)
is -0.5604,
coefficient of correlation of predicted data set
( _
) is 0.7252 and degree of freedom is 26 by
Partial Least Squares Regression technique.
QSAR Modeling of Bisbenzofuran Compounds using 2D-Descriptors as Antimalarial...ijtsrd
In the present study we have performed Quantitative structure activity relationship (QSAR) analysis for 43bisbenzofuran derivatives to estimate the antimalarial activity using some 2D descriptors. Several significant QSAR models has been calculated for predicting the antimalarial activity (“logIC50) of these molecules by using the multiple linear regression (MLR) technique. Among the obtained QSAR models, a four parametric model was most significant having R2=0.9502. An external set was used for confirming the predictive power of the models. High correlation between experimental and predicted antimalarial activity values, was obtained in the validation approach that displayed the good modality of the derived QSAR models. Tripti Kaushal | Anita K | Bashirulla Shaik | Vijay K. Agrawal"QSAR Modeling of Bisbenzofuran Compounds using 2D-Descriptors as Antimalarial Agents" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd9497.pdf http://www.ijtsrd.com/chemistry/other/9497/qsar-modeling-of-bisbenzofuran-compounds-using-2d-descriptors-as-antimalarial-agents/tripti-kaushal
Physical and Structural Characterization of Biofield Treated Imidazole Deriva...albertdivis
The Aim of present study was to evaluate the impact of biofield treatment on two imidazole derivatives (i.e., imidazole and 2-methylimidazole) by various analytical methods.
Novel Hybrid Molecules of Isoxazole Chalcone Derivatives: Synthesis and Study...Ratnakaram Venkata Nadh
medicine due to their significant role in the treatment of different health problems.
Methods: We have synthesized new series of isoxazole-chalcone conjugates (14a-m) by the
Claisen-Schmidt condensation of suitable substituted acetophenones with isoxazole aldehydes (12a-d).
In vitro cytotoxic activity of the synthesized compounds was studied against four different selected
human cancer cell lines by using sulforhodamine B (SRB) method.
Results: The adopted scheme resulted in good yields of new series of isoxazole-chalcone
conjugates (14a-m). Potent cytotoxic activity was observed for compounds -14a, 14b, 14e, 14i, 14j
and 14k against prostate DU-145 cancer cell line.
Conclusion: The observed potent cytotoxic activities were due to the presence of 3,4,5-
trimethoxyphenyl group.
A STUDY TO EVALUATE THE IN VITRO ANTIMICROBIAL ACTIVITY AND ANTIANDROGENIC E...Dr. Pradeep mitharwal
The present paper deals with synthesis and characterization
of some new chromium (III) Schiff base complexes using microwave irradiation
technique as well as conventional heating. The S∩N donor benzothiazolines, 1-
(2-furanyl) ethanone benzothiazoline (Bzt1N
∩
SH), 1-(2-thienyl) ethanone
benzothiazoline (Bzt2N
∩
SH) and 1-(2-pyridyl) ethanone benzothiazoline
(Bzt3N
∩
SH) were prepared by the condensation of ortho-aminothiophenol with
respective ketones in ethanol.
QSAR Modeling of Bisbenzofuran Compounds using 2D-Descriptors as Antimalarial...ijtsrd
In the present study we have performed Quantitative structure activity relationship (QSAR) analysis for 43bisbenzofuran derivatives to estimate the antimalarial activity using some 2D descriptors. Several significant QSAR models has been calculated for predicting the antimalarial activity (“logIC50) of these molecules by using the multiple linear regression (MLR) technique. Among the obtained QSAR models, a four parametric model was most significant having R2=0.9502. An external set was used for confirming the predictive power of the models. High correlation between experimental and predicted antimalarial activity values, was obtained in the validation approach that displayed the good modality of the derived QSAR models. Tripti Kaushal | Anita K | Bashirulla Shaik | Vijay K. Agrawal"QSAR Modeling of Bisbenzofuran Compounds using 2D-Descriptors as Antimalarial Agents" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd9497.pdf http://www.ijtsrd.com/chemistry/other/9497/qsar-modeling-of-bisbenzofuran-compounds-using-2d-descriptors-as-antimalarial-agents/tripti-kaushal
Physical and Structural Characterization of Biofield Treated Imidazole Deriva...albertdivis
The Aim of present study was to evaluate the impact of biofield treatment on two imidazole derivatives (i.e., imidazole and 2-methylimidazole) by various analytical methods.
Novel Hybrid Molecules of Isoxazole Chalcone Derivatives: Synthesis and Study...Ratnakaram Venkata Nadh
medicine due to their significant role in the treatment of different health problems.
Methods: We have synthesized new series of isoxazole-chalcone conjugates (14a-m) by the
Claisen-Schmidt condensation of suitable substituted acetophenones with isoxazole aldehydes (12a-d).
In vitro cytotoxic activity of the synthesized compounds was studied against four different selected
human cancer cell lines by using sulforhodamine B (SRB) method.
Results: The adopted scheme resulted in good yields of new series of isoxazole-chalcone
conjugates (14a-m). Potent cytotoxic activity was observed for compounds -14a, 14b, 14e, 14i, 14j
and 14k against prostate DU-145 cancer cell line.
Conclusion: The observed potent cytotoxic activities were due to the presence of 3,4,5-
trimethoxyphenyl group.
A STUDY TO EVALUATE THE IN VITRO ANTIMICROBIAL ACTIVITY AND ANTIANDROGENIC E...Dr. Pradeep mitharwal
The present paper deals with synthesis and characterization
of some new chromium (III) Schiff base complexes using microwave irradiation
technique as well as conventional heating. The S∩N donor benzothiazolines, 1-
(2-furanyl) ethanone benzothiazoline (Bzt1N
∩
SH), 1-(2-thienyl) ethanone
benzothiazoline (Bzt2N
∩
SH) and 1-(2-pyridyl) ethanone benzothiazoline
(Bzt3N
∩
SH) were prepared by the condensation of ortho-aminothiophenol with
respective ketones in ethanol.
QSAR Studies of the Inhibitory Activity of a Series of Substituted Indole and...inventionjournals
HF method, with the basis set 6-31G (d) was employed to calculate quantum some chemical descriptors of 37 substituted Indole. The best descriptors were selected to establish the quantitative structure activity relationship (QSAR) of the inhibitory activity against isoprenylcysteine carboxyl methyltransferase (Icmt), by principal components analysis (PCA), to a multiple regression analysis (MLR), to a nonlinear regression (RNLM) and to an artificial neural network (ANN). We accordingly propose a quantitative model and we interpret the activity of the compounds relying on the multivariate statistical analysis. This study shows that the MLR and have served to predict activity, but when compared with the results given by the ANN model. We concluded that the predictions achieved by this latter is more effective and much better than other models. The statistical results indicate that the model is statistically significant and shows very good stability towards data variation in the validation method. The contribution of each descriptor to the structure-activity relationship is evaluated.
Studies on Anti-Inflammation Activity of Phenols Using Newly Introduced Balab...IOSRJAC
The interated ‘sum’ versus ‘product’ operation concept recently introduced by Balaban-KhadikarSufia yielding newly introduced F and G indices, has been used for proposing most significant QSAR model for modeling anti inflammatory activity of phenol. Results are discussed on the basis of well known statistical parameters.
Novel Hybrid Molecules of Quinazoline Chalcone Derivatives: Synthesis and Stu...Ratnakaram Venkata Nadh
Abstract: Background: A new series of quinazoline linked chalcone conjugates were synthesized
and evaluated for their in vitro cytotoxicity.
Methods: The quinazoline-chalcone derivatives (13a-r) have been prepared by the Claisen-Schmidt
condensation of various substituted benzaldehydes (12a-r) with substituted l-(4-(3,4-
dihydroquinazolin-4-ylamino)phenyl)ethanone (11a-b) in the presence of aqueous NaOH. Three
potential compounds 13f, 13g and 13h exhibited cytotoxicity against leukemia (GI50 value of
1.07, 0.26 and 0.24 μM), Non-small lung (GI50 values of 2.05,1.32 and 0.23 μM), colon (GI50
values of 0.54, 0.34 and 0.34 μM) and breast (GI50 values of 2.17, 1.84 and 0.22 μM) cell line,
respectively.
Results and Conclusion: Based on these biological results, it is evident that compound 13h has the
potential to be considered for further detailed studies either alone or in combination with existing
therapies as potential anticancer agents.
Inhibition of Aldose Activity by Essential Phytochemicals of Cymbopogon citra...CSCJournals
The ambiguity of whether aldose reductase, an enzyme of polyol pathway, is linked to diabetes and its complication has been receded based on the recent studies made on the inhibition of its (Aldose reductase) activity. In our current study, we have used an in silico approach (molecular docking) to analyze the effect of essential phytochemicals obtained from Cymbopogon citratus on the aldose reductase activity. C.citratus is grown extensively in tropical countries including India for perfumery and pharmaceuticals. The essential phytochemicals of C.citratus like Myrcene, Citral, and Geraniol have been used as ligand for the molecular docking analysis with Aldose reductase as receptor. The docking analysis showed Myrcene, with binding energy of -8.76 Kcal/mol is best amongst Citral and Geraniol which are having binding energies of -7.24 Kcal/mol and -7.93 Kcal/mol respectively for inhibiting the activity of Aldose reductase.
Qsar studies on gallic acid derivatives and molecular docking studies of bace...bioejjournal
It is reported that Alzheimer disease is linked with hypertension, diabetes type 2 and high cholesterolemia. The underlying genetic cause relating these diseases are not well studied clinically. But it has been widely
accepted that beta secretase (BACE1) is the main culprit of causing Alzheimer disease. This enzyme comes under peptidase A1 family. In the present work, ligand based and structure based drug designing have been reported. QSAR studies were done using 21 gallic acid derivatives dataset to develop good predictive
model in order to predict biological activity and certain descriptors was reported to further enhance the
analgesic activity of gallic acid derivatives. Molecular docking studies were performed in order to find
structure based drug design. Two natural gallic acid derivative have been repoted as a potent inhibitor to beta secretase enzyme.
Qsar Studies on Gallic Acid Derivatives and Molecular Docking Studies of Bace...bioejjournal
It is reported that Alzheimer disease is linked with hypertension, diabetes type 2 and high cholesterolemia. The underlying genetic cause relating these diseases are not well studied clinically. But it has been widely accepted that beta secretase (BACE1) is the main culprit of causing Alzheimer disease. This enzyme comes under peptidase A1 family. In the present work, ligand based and structure based drug designing have been
reported. QSAR studies were done using 21 gallic acid derivatives dataset to develop good predictive model in order to predict biological activity and certain descriptors was reported to further enhance the analgesic activity of gallic acid derivatives. Molecular docking studies were performed in order to find
structure based drug design. Two natural gallic acid derivative have been repoted as a potent inhibitor to beta secretase enzyme.
Statistical Optimization of Keratinase Production from Marine FungusIJERA Editor
To improve the yield of keratinase from marine fungus Scopulariopsis brevicaulis, different medium constituents were optimized using response surface methodology (RSM) based on central composite design (CCD). The strain produced 24.8U/mL and 36.4U/mL of keratinase activity in conventional method of optimization with glucose and soya bean meal as carbon and nitrogen sources. Response surface methodology which was applied to optimize concentrations of glucose, soya bean meal, feather powder and inoculum level, improved the productivity to 225.0U/mL. This value represents 6.18 fold increases in productivity as compared to conventional methods. Optimal parameters of the cultivation process were determined as glucose 1.52g/L, soya bean meal-1.08g/L, feather powder-1.04g/L and inoculum level-10.6%.
quantitative structure activity relationship studies of anti proliferative ac...IJEAB
Many studies have focused on indole derivatives mainly their antiproliferative effect. The therapeutic effect of this group of molecule is very important. Quantitative structure–activity relationships (QSAR) have been applied for development relationships between physicochemical properties and their biological activities. A series of 30 molecules derived from indole is based on the quantitative structure-activity relationship (QSAR). This study was carried out using the principal component analysis (PCA) method, the multiple linear regression method (MLR), non-linear regression (RNLM), the artificial neural network (ANN) and it was validated using cross validation analysis (CV). We accordingly propose a quantitative model and we try to interpret the activity of the compounds relying on the multivariate statistical analyses. A theoretical study of series was studied using density functional theory (DFT) calculations at B3LYP/6-31G(d) level of theory for employing to calculate electronic descriptors when, the topological descriptors were computed with ACD/ChemSketch and ChemDraw 8.0 programs. The best QSAR model was found in agreement with the experimental by ANN (R = 0,99).
A simple visible spectrophotometric method is proposed for the determination
of ulipristal acetate present in bulk and tablet formulation. The currently
proposed method is established based on MBTH oxidation by ferric ions to
form an active coupling species (electrophile), followed by its coupling with
the ulipristal in acidic medium to form high intensiϑied green colored chromophore
having max at 609 nm. Validated the method as per the current
guidelines of ICH. Beer’s law was obeyed in the concentration range of 6.25 –
37.50 g mL 1 with a high regression coefϑicient (r > 0.999). Reproducibility,
accuracy, and precision of the method are evident from the low values of R.S.D.
This method can be used in quality control laboratories for routine analysis of
ulipristal acetate in bulk drug and pharmaceutical dosage forms.
QSAR Studies of the Inhibitory Activity of a Series of Substituted Indole and...inventionjournals
HF method, with the basis set 6-31G (d) was employed to calculate quantum some chemical descriptors of 37 substituted Indole. The best descriptors were selected to establish the quantitative structure activity relationship (QSAR) of the inhibitory activity against isoprenylcysteine carboxyl methyltransferase (Icmt), by principal components analysis (PCA), to a multiple regression analysis (MLR), to a nonlinear regression (RNLM) and to an artificial neural network (ANN). We accordingly propose a quantitative model and we interpret the activity of the compounds relying on the multivariate statistical analysis. This study shows that the MLR and have served to predict activity, but when compared with the results given by the ANN model. We concluded that the predictions achieved by this latter is more effective and much better than other models. The statistical results indicate that the model is statistically significant and shows very good stability towards data variation in the validation method. The contribution of each descriptor to the structure-activity relationship is evaluated.
Studies on Anti-Inflammation Activity of Phenols Using Newly Introduced Balab...IOSRJAC
The interated ‘sum’ versus ‘product’ operation concept recently introduced by Balaban-KhadikarSufia yielding newly introduced F and G indices, has been used for proposing most significant QSAR model for modeling anti inflammatory activity of phenol. Results are discussed on the basis of well known statistical parameters.
Novel Hybrid Molecules of Quinazoline Chalcone Derivatives: Synthesis and Stu...Ratnakaram Venkata Nadh
Abstract: Background: A new series of quinazoline linked chalcone conjugates were synthesized
and evaluated for their in vitro cytotoxicity.
Methods: The quinazoline-chalcone derivatives (13a-r) have been prepared by the Claisen-Schmidt
condensation of various substituted benzaldehydes (12a-r) with substituted l-(4-(3,4-
dihydroquinazolin-4-ylamino)phenyl)ethanone (11a-b) in the presence of aqueous NaOH. Three
potential compounds 13f, 13g and 13h exhibited cytotoxicity against leukemia (GI50 value of
1.07, 0.26 and 0.24 μM), Non-small lung (GI50 values of 2.05,1.32 and 0.23 μM), colon (GI50
values of 0.54, 0.34 and 0.34 μM) and breast (GI50 values of 2.17, 1.84 and 0.22 μM) cell line,
respectively.
Results and Conclusion: Based on these biological results, it is evident that compound 13h has the
potential to be considered for further detailed studies either alone or in combination with existing
therapies as potential anticancer agents.
Inhibition of Aldose Activity by Essential Phytochemicals of Cymbopogon citra...CSCJournals
The ambiguity of whether aldose reductase, an enzyme of polyol pathway, is linked to diabetes and its complication has been receded based on the recent studies made on the inhibition of its (Aldose reductase) activity. In our current study, we have used an in silico approach (molecular docking) to analyze the effect of essential phytochemicals obtained from Cymbopogon citratus on the aldose reductase activity. C.citratus is grown extensively in tropical countries including India for perfumery and pharmaceuticals. The essential phytochemicals of C.citratus like Myrcene, Citral, and Geraniol have been used as ligand for the molecular docking analysis with Aldose reductase as receptor. The docking analysis showed Myrcene, with binding energy of -8.76 Kcal/mol is best amongst Citral and Geraniol which are having binding energies of -7.24 Kcal/mol and -7.93 Kcal/mol respectively for inhibiting the activity of Aldose reductase.
Qsar studies on gallic acid derivatives and molecular docking studies of bace...bioejjournal
It is reported that Alzheimer disease is linked with hypertension, diabetes type 2 and high cholesterolemia. The underlying genetic cause relating these diseases are not well studied clinically. But it has been widely
accepted that beta secretase (BACE1) is the main culprit of causing Alzheimer disease. This enzyme comes under peptidase A1 family. In the present work, ligand based and structure based drug designing have been reported. QSAR studies were done using 21 gallic acid derivatives dataset to develop good predictive
model in order to predict biological activity and certain descriptors was reported to further enhance the
analgesic activity of gallic acid derivatives. Molecular docking studies were performed in order to find
structure based drug design. Two natural gallic acid derivative have been repoted as a potent inhibitor to beta secretase enzyme.
Qsar Studies on Gallic Acid Derivatives and Molecular Docking Studies of Bace...bioejjournal
It is reported that Alzheimer disease is linked with hypertension, diabetes type 2 and high cholesterolemia. The underlying genetic cause relating these diseases are not well studied clinically. But it has been widely accepted that beta secretase (BACE1) is the main culprit of causing Alzheimer disease. This enzyme comes under peptidase A1 family. In the present work, ligand based and structure based drug designing have been
reported. QSAR studies were done using 21 gallic acid derivatives dataset to develop good predictive model in order to predict biological activity and certain descriptors was reported to further enhance the analgesic activity of gallic acid derivatives. Molecular docking studies were performed in order to find
structure based drug design. Two natural gallic acid derivative have been repoted as a potent inhibitor to beta secretase enzyme.
Statistical Optimization of Keratinase Production from Marine FungusIJERA Editor
To improve the yield of keratinase from marine fungus Scopulariopsis brevicaulis, different medium constituents were optimized using response surface methodology (RSM) based on central composite design (CCD). The strain produced 24.8U/mL and 36.4U/mL of keratinase activity in conventional method of optimization with glucose and soya bean meal as carbon and nitrogen sources. Response surface methodology which was applied to optimize concentrations of glucose, soya bean meal, feather powder and inoculum level, improved the productivity to 225.0U/mL. This value represents 6.18 fold increases in productivity as compared to conventional methods. Optimal parameters of the cultivation process were determined as glucose 1.52g/L, soya bean meal-1.08g/L, feather powder-1.04g/L and inoculum level-10.6%.
quantitative structure activity relationship studies of anti proliferative ac...IJEAB
Many studies have focused on indole derivatives mainly their antiproliferative effect. The therapeutic effect of this group of molecule is very important. Quantitative structure–activity relationships (QSAR) have been applied for development relationships between physicochemical properties and their biological activities. A series of 30 molecules derived from indole is based on the quantitative structure-activity relationship (QSAR). This study was carried out using the principal component analysis (PCA) method, the multiple linear regression method (MLR), non-linear regression (RNLM), the artificial neural network (ANN) and it was validated using cross validation analysis (CV). We accordingly propose a quantitative model and we try to interpret the activity of the compounds relying on the multivariate statistical analyses. A theoretical study of series was studied using density functional theory (DFT) calculations at B3LYP/6-31G(d) level of theory for employing to calculate electronic descriptors when, the topological descriptors were computed with ACD/ChemSketch and ChemDraw 8.0 programs. The best QSAR model was found in agreement with the experimental by ANN (R = 0,99).
A simple visible spectrophotometric method is proposed for the determination
of ulipristal acetate present in bulk and tablet formulation. The currently
proposed method is established based on MBTH oxidation by ferric ions to
form an active coupling species (electrophile), followed by its coupling with
the ulipristal in acidic medium to form high intensiϑied green colored chromophore
having max at 609 nm. Validated the method as per the current
guidelines of ICH. Beer’s law was obeyed in the concentration range of 6.25 –
37.50 g mL 1 with a high regression coefϑicient (r > 0.999). Reproducibility,
accuracy, and precision of the method are evident from the low values of R.S.D.
This method can be used in quality control laboratories for routine analysis of
ulipristal acetate in bulk drug and pharmaceutical dosage forms.
INTEGRATED, RELIABLE AND CLOUD-BASED PERSONAL HEALTH RECORD: A SCOPING REVIEWhiij
Personal Health Records (PHR) emerge as an alternative to integrate patient’s health information to give a
global view of patients' status. However, integration is not a trivial feature when dealing with a variety
electronic health systems from healthcare centers. Access to PHR sensitive information must comply with
privacy policies defined by the patient. Architecture PHR design should be in accordance to these, and take
advantage of nowadays technology. Cloud computing is a current technology that provides scalability,
ubiquity, and elasticity features. This paper presents a scoping review related to PHR systems that achieve
three characteristics: integrated, reliable and cloud-based. We found 101 articles that addressed
thosecharacteristics. We identified four main research topics: proposal/developed systems, PHR
recommendations for development, system integration and standards, and security and privacy. Integration
is tackled with HL7 CDA standard. Information reliability
Overview of critical factors affecting medical user interfaces in intensive c...hiij
This paper provides a comprehensive overview of cri
tical factors, which affect on-screen user interfac
es of
medical devices in Intensive Care Unit (ICU). A lit
erature survey with relevant research publications
has
led to selection of thirty eight critical factors i
n ICU. The critical factors identified are categori
zed into
various groups based on three major aspects – syste
m evaluation parameters, constituents of patient
management and user interface design. Physicians’ s
urvey, in which five physicians are involved, is us
ed to
categorize the identified critical factors into rel
ated groups. In the process, fourteen critical fact
ors are
mainly selected, which affect on-screen user interf
ace design of medical devices. The applicability of
such
factors is demonstrated with the help of a case stu
dy of head-injury patient admitted in ICU. The crit
ical
factors identified are definitely useful to device
manufacturers, user interface designers, ICU
administrators and physicians for improved device d
esign, ICU resource management and patient care.
A look at the five years since the start of www.publiclibrariesnews.com, including lessons learnt in producing and informative website and in dealing with the media.
PREDICTIVE COMPARATIVE QSAR ANALYSIS OF AS 5-NITROFURAN-2-YL DERIVATIVES MYCO...hiij
Antitubercular activity of 5-nitrofuran-2-yl Derivatives series were subjected to Quantitative Structure
Activity Relationship (QSAR) Analysis with an effort to derive and understand a correlation between the
biological activity as response variable and different molecular descriptors as independent variables.
QSAR models are built using 40 molecular descriptor dataset. Different statistical regression expressions
were got using Partial Least Squares (PLS) ,Multiple Linear Regression (MLR) and Principal Component
Regression (PCR) techniques. The among these technique, Partial Least Square Regression (PLS)
technique has shown very promising result as compared to MLR technique A QSAR model was build by a
training set of 30 molecules with correlation coefficient (
) of 0.8484 , significant cross validated
correlation coefficient (
) is 0.0939, is 48.5187,
for external test set (
_
) is -0.5604,
coefficient of correlation of predicted data set (
_
) is 0.7252 and degree of freedom is 26 by
Partial Least Squares Regression technique.
A Semi-empirical based QSAR study of indole휷- Diketo acid, Diketo acid and Ca...iosrjce
In this study, a set of novel synthesized indole훽- diketo acid, diketo acid and carboxamide
derivativeswas investigated by quantitative structure–activity relationship (QSAR) analysis using semiempirical
(PM3) based descriptors. The best molecular descriptors identified were LogP, polar area
corresponding to absolute values of electrostatic potential greater than 75 (P-area(75)), Energy (E), Minimum
values of electrostatic potential (as mapped onto an electron density surface) (MinEIPot), Polar surface area
and Maximum values of electrostatic potential (as mapped onto an electron density surface) (MaxEIPot) that
contributed to the anti-HIV activity of the indole훽- diketo acid, diketo acid and carboxamide derivatives as
independent factors. The correlation of these descriptors with their anti-HIV activity increases indicating their
importance in studying biological activity. Quantitative structure activity relationship (QSAR) analysis was
applied to 37 of the above mentioned derivatives using physicochemical and structural molecular descriptors
obtained by the semi-empirical method by employing PM3 basis set. By using the multiple linear regression
(MLR) technique several QSAR models have been drown up with the help these calculated descriptors and the
anti-HIV activity of indole훽- diketo acid, diketo acid and carboxamide derivatives. The regression method was
used to derive the most significant models as a calibration model for predicting the LogIC50 of this class of
compounds. Among the obtained QSAR models presented in the study from the MLR method, statistically the
most significant one is the last model with the squared correlation coefficient 0.8932, Q = 3.1854 and F=
27.8644 that could be useful to predict the biological activity of indole훽- diketo acid, diketo acid and carboxamide derivatives as Potent HIV-1 Drugs.
Effect of 3D parameters on Antifungal Activities of Some Heterocyclic CompoundsIOSR Journals
Quantitative Structure Activity Relationships (QSAR) of some heterocyclic compounds was studied using some 3D parameters. The QSAR models indicated that Dipole Y, Dipole mag., Y length and some indicator parameters are very effective in describing the antifungal activities of these compounds against Candida albicans in the training and external test set. The multiple regression analysis have produced well predictive statistically significant and cross validated QSAR models which help to explore some expectedly potent compounds.
Development of machine learning-based prediction models for chemical modulato...Sunghwan Kim
Presented at the 2018 Research Festival at the National Institutes of Health (NIH) in Bethesda, MD (September 13, 2018).
==== Abstract ====
The retinoid X receptor (RXR) is a nuclear hormone receptor that functions as a transcription factor with roles in development, cell differentiation, metabolism, and cell death. Chemicals that interfere the RXR signaling pathway may cause adverse effects on human health. In this study, public-domain bioactivity data available in PubChem (https://pubchem.ncbi.nlm.nih.gov) were used to develop machine learning-based prediction models for chemical modulators of RXR-alpha, which is a subtype of RXR that plays a role in metabolic signaling pathways, dermal cysts, cardiac development, insulin sensitization, etc. The models were constructed from quantitative high-throughput screening (qHTS) data from the Tox21 project, using popular supervised machine learning methods (including support vector machine, random forest, neural network, k-nearest neighbors, decision tree, and naïve Bayes). The general applicability of the developed models was evaluated with external data sets from ChEMBL and the NCATS Chemical Genomics Center (NCGC). This study showcases how open data in the public domain can be used to develop prediction models for bioactivity of small molecules.
Qsar Studies on Gallic Acid Derivatives and Molecular Docking Studies of Bace...bioejjournal
It is reported that Alzheimer disease is linked with hypertension, diabetes type 2 and high cholesterolemia.
The underlying genetic cause relating these diseases are not well studied clinically. But it has been widely
accepted that beta secretase (BACE1) is the main culprit of causing Alzheimer disease. This enzyme comes
under peptidase A1 family. In the present work, ligand based and structure based drug designing have been
reported. QSAR studies were done using 21 gallic acid derivatives dataset to develop good predictive
model in order to predict biological activity and certain descriptors was reported to further enhance the
analgesic activity of gallic acid derivatives. Molecular docking studies were performed in order to find structure based drug design. Two natural gallic acid derivative have been repoted as a potent inhibitor to beta secretase enzyme.
2017 - Plausible Bioindicators of Biological Nitrogen Removal Process in WWTPsWALEBUBLÉ
Reference:
Zornoza, A., Alonso, J.L. and Serrano, S. (2017) Plausible Bioindicators of Biological Nitrogen Removal Process in WWTPs. In: Abstracts of the 7th congress of European microbiologists FEMS 2017, Valencia, Spain, 9-13 July 2017.
QSAR studies of some anilinoquinolines for their antitumor activity as EGFR i...IOSR Journals
Quantitative Structure-Activity Relationship studies has been performed on some anilinoquinolines . A variety of parameters including 2D- autocorelation, RDF, 3D- MoRSE, WHIM and GETAWAY parameters have been chosen for modeling the antitumor activity of these compounds. The multiple regression analysis reveals that the seven –parametric model is the best for modeling the activity of the compounds under present study. This model has been tested by using cross validated parameters. The results are also discussed on the basis of ridge regression.
7 synthesis, characterisation and antimicrobial activity of schiff base of 7 ...BIOLOGICAL FORUM
ABSTRACT: Compounds having 2-quinolone moiety are associated with interesting biological activities. In the present study, we synthesized Schiff bases of 7-hydroxy-3-methyl-2-quinolone and their antibacterial activity was evaluated by wells diffusion method. Schiff bases of 7-hydroxy-3-methyl-2-quinolone (1 to 5 named as Q2aa-Q2ae) were prepared by refluxing 7-hydroxy-3-methyl-2-quinolone with substituted aromatic aldehydes. The final test compounds were purified and characterized by IR, 1HNMR and Mass Spectral studies. M.P. of these compounds was confirmed by open capillary method instrument chemline cl 725. They were evaluated for antibacterial activity. Compounds were active against Klebsiella pneumonia and Enterococcus faecalis. While ciprofloxacin was used as standards.
Exploration of a potential FtsZ inhibitors as new scaffolds by Ligand and Str...Pavan Kumar
Multi-drug resistant Mtb is a major worldwide health problem. Therefore, it is need to develop new antibiotics with novel modes of action to overcome this emerging resistance problem.
FtsZ (Filamentous temperature-sensitive protein Z ) Drug Target for Tuberculosis
FtsZ is the key protein of bacterial cell division, filament-forming GTPase and a structural homologue of eukaryotic tubulin.
It interacts with membrane-associated proteins FtsA and ZipA and assembles into a ring like structure at the midcell, this ring is known as Z-ring.
The formation of the Z-ring is facilitated by the ability of FtsZ to bind to GTP, which enables polymerization of FtsZ, resulting in the creation of straight protofilaments.
It is the first protein to move to the division site, and is essential for recruiting other proteins that produce a new cell wall between the dividing cells. So it is an emergent target for new antibiotics.
Here you will know , how to write a critical review & what sections are suppose to analyse why reading a paper. Here is a critical review of a paper titled ' Repeated dose multi-drug testing using a microfluidic chip-based
coculture of human liver and kidney proximal tubules equivalents' published recently in 2020 in reputed journal nature.
Similar to Predictive comparative qsar analysis of as 5 nitrofuran-2-yl derivatives myco bacterium tuberculosis h37 rv inhibitors (20)
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
HEALTH DISPARITIES: DIFFERENCES IN VETERAN AND NON-VETERAN POPULATIONS USING ...hiij
Introduction: This study investigated self-reported health status, health screenings, vision problems, and
vaccination rates among veteran and non-veteran groups to uncover health disparities that are critical for
informed health system planning for veteran populations.
Methods: Using public-use data from the National Health Interview Survey (2015-2018), this study adopts
an ecologic cross-sectional approach to conduct an in-depth analysis and visualization of the data assisted
by Generative AI, specifically ChatGPT-4. This integration of advanced AI tools with traditional
epidemiological principles enables systematic data management, analysis, and visualization, offering a
nuanced understanding of health dynamics across demographic segments and highlighting disparities
essential for veteran health system planning.
Findings: Disparities in self-reports of health outcomes, health screenings, vision problems, and
vaccination rates were identified, emphasizing the need for targeted interventions and policy adjustments.
Conclusion: Insights from this study could inform health system planning, using epidemiological data
assessment to suggest enhancements for veteran healthcare delivery. These findings highlight the value of
integrating Generative AI with epidemiological analysis in shaping public health policy and health
planning.
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
BRIEF COMMENTARY: USING A LOGIC MODEL TO INTEGRATE PUBLIC HEALTH INFORMATICS ...hiij
The COVID-19 pandemic has been a watershed moment in public health surveillance, highlighting the
crucial role of data-driven insights in informing health actions and policies. Revisiting key concepts—
public health, epidemiology in public health practice, public health surveillance, and public health
informatics—lays the foundation for understanding how these elements converge to create a robust public
health surveillance system framework. Especially during the COVID-19 pandemic, this integration was
exemplified by the WHO efforts in data dissemination and the subsequent global response. The role of
public health informatics emerged as instrumental in this context, enhancing data collection, management,
analysis, interpretation, and dissemination processes. A logic model for public health surveillance systems
encapsulates the integration of these concepts. It outlines the inputs and outcomes and emphasizes the
crucial actions and resources for effective system operation, including the imperative of training and
capacity development.
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
AUTOMATIC AND NON-INVASIVE CONTINUOUS GLUCOSE MONITORING IN PAEDIATRIC PATIENTShiij
Glycated haemoglobin does not allow you to highlight the effects that food choices, physical activity and
medications have on your glycaemic control day by day. The best way to monitor and keep track of the
immediate effects that these have on your blood sugar levels is self-monitoring, therefore the use of a
glucometer. Thanks to this tool you have the possibility to promptly receive information that helps you to
intervene in the most appropriate way, bringing or keeping your blood sugar levels as close as possible to
the reference values indicated by your doctor. Currently, blood glucose meters are used to measure and
control blood glucose. Diabetes is a fairly complex disease and it is important for those who suffer from it
to check their blood sugar (blood sugar) periodically throughout the day to prevent dangerous
complications. Many children newly diagnosed with diabetes and their families may face unique challenges
when dealing with the everyday management of diabetes, including treatments, adapting to dietary
changes, and the routine monitoring of blood glucose. Many questions may also arise when selecting a
blood glucose meter for paediatric patients. With current blood glucose meters, even with multiple daily
self-tests, high and low blood glucose levels may not be detected. Key factors that may be considered when
selecting a meter include accuracy of the meter; size of the meter; small sample size required for testing;
ease of use and easy-to-follow testing procedure; ability for alternate testing sites; quick testing time and
availability of results; ease of portability to allow testing at school and during leisure time; easyto- read
numbers on display; memory options; cost of meter and supplies. In this study we will show a new
automatic portable, non-invasive device and painless for the daily continuous monitoring (24 hours a day)
of blood glucose in paediatric patients.
INTEGRATING MACHINE LEARNING IN CLINICAL DECISION SUPPORT SYSTEMShiij
This review article examines the role of machine learning (ML) in enhancing Clinical Decision Support
Systems (CDSSs) within the modern healthcare landscape. Focusing on the integration of various ML
algorithms, such as regression, random forest, and neural networks, the review aims to showcase their
potential in advancing patient care. A rapid review methodology was utilized, involving a survey of recent
articles from PubMed and Google Scholar on ML applications in healthcare. Key findings include the
demonstration of ML's predictive power in patient outcomes, its ability to augment clinician knowledge,
and the effectiveness of ensemble algorithmic approaches. The review highlights specific applications of
diverse ML models, including moment kernel machines in predicting surgical outcomes, k-means clustering
in simplifying disease phenotypes, and extreme gradient boosting in estimating injury risk. Emphasizing
the potential of ML to tackle current healthcare challenges, the article highlights the critical role of ML in
evolving CDSSs for improved clinical decision-making and patient care. This comprehensive review also
addresses the challenges and limitations of integrating ML into healthcare systems, advocating for a
collaborative approach to refine these systems for safety, efficacy, and equity.
BRIEF COMMENTARY: USING A LOGIC MODEL TO INTEGRATE PUBLIC HEALTH INFORMATICS ...hiij
The COVID-19 pandemic has been a watershed moment in public health surveillance, highlighting the
crucial role of data-driven insights in informing health actions and policies. Revisiting key concepts—
public health, epidemiology in public health practice, public health surveillance, and public health
informatics—lays the foundation for understanding how these elements converge to create a robust public
health surveillance system framework. Especially during the COVID-19 pandemic, this integration was
exemplified by the WHO efforts in data dissemination and the subsequent global response. The role of
public health informatics emerged as instrumental in this context, enhancing data collection, management,
analysis, interpretation, and dissemination processes. A logic model for public health surveillance systems
encapsulates the integration of these concepts. It outlines the inputs and outcomes and emphasizes the
crucial actions and resources for effective system operation, including the imperative of training and
capacity development.
INTEGRATING MACHINE LEARNING IN CLINICAL DECISION SUPPORT SYSTEMShiij
This review article examines the role of machine learning (ML) in enhancing Clinical Decision Support
Systems (CDSSs) within the modern healthcare landscape. Focusing on the integration of various ML
algorithms, such as regression, random forest, and neural networks, the review aims to showcase their
potential in advancing patient care. A rapid review methodology was utilized, involving a survey of recent
articles from PubMed and Google Scholar on ML applications in healthcare. Key findings include the
demonstration of ML's predictive power in patient outcomes, its ability to augment clinician knowledge,
and the effectiveness of ensemble algorithmic approaches. The review highlights specific applications of
diverse ML models, including moment kernel machines in predicting surgical outcomes, k-means clustering
in simplifying disease phenotypes, and extreme gradient boosting in estimating injury risk. Emphasizing
the potential of ML to tackle current healthcare challenges, the article highlights the critical role of ML in
evolving CDSSs for improved clinical decision-making and patient care. This comprehensive review also
addresses the challenges and limitations of integrating ML into healthcare systems, advocating for a
collaborative approach to refine these systems for safety, efficacy, and equity.
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
The Proposed Guidelines for Cloud Computing Migration for South African Rural...hiij
It is now overdue for the hospitals in South African rural areas to implement cloud computing technologies in order to access patient data quickly in an emergency. Sometimes medical practitioners take time to attend patients due to the unavailability of kept records, leading to either a loss of time or the reassembling of processes to recapture lost patient files. However, there are few studies that highlight challenges faced by rural hospitals but they do not recommend strategies on how they can migrate to cloud computing. The purpose of this paper was to review recent papers about the critical factors that influence South African hospitals in adopting cloud computing. The contribution of the study is to lay out the importance of cloud computing in the health sectors and to suggest guidelines that South African rural hospitals can follow in order to successfully relocate into cloud computing.The existing literature revealed that Hospitals may enhance their record-keeping procedures and conduct business more effectively with the help of the cloud computing. In conclusion, if hospitals in South African rural areas is to fully benefit from cloud-based records management systems, challenges relating to data storage, privacy, security, and the digital divide must be overcome.
SUPPORTING LARGE-SCALE NUTRITION ANALYSIS BASED ON DIETARY SURVEY DATAhiij
While online survey systems facilitate the collection on copious records on diet, exercise and other healthrelated data, scientists and other public health experts typically must download data from those systems
into external tools for conducting statistical analyses. A more convenient approach would enable
researchers to perform analyses online, without the need to coordinate additional analysis tools. This
paper presents a system illustrating such an approach, using as a testbed the WAVE project, which is a 5-
year childhood obesity prevention initiative being conducted at Oregon State University by health scientists
utilizing a web application called WavePipe. This web application has enabled health scientists to create
studies, enrol subjects, collect physical activity data, and collect nutritional data through online surveys.
This paper presents a new sub-system that enables health scientists to analyse and visualize nutritional
profiles based on large quantities of 24-hour dietary recall records for sub-groups of study subjects over
any desired period of time. In addition, the sub-system enables scientists to enter new food information
from food composition databases to build a comprehensive food profile. Interview feedback from novice
health science researchers using the new functionality indicated that it provided a usable interface and
generated high receptiveness to using the system in practice.
AN EHEALTH ADOPTION FRAMEWORK FOR DEVELOPING COUNTRIES: A SYSTEMATIC REVIEWhiij
#Health #clinic #education #StaySafe #pharmacy #healthylifestyle
call for papers..!
-----------------------------
Health Informatics: An International Journal (HIIJ)
ISSN : 2319 - 2046 (Online); 2319 - 3190 (Print)
Here's where you can reach us : hiij@aircconline.com
visit us on : https://airccse.org/journal/hiij/index.html
**************
published articles..!
AN EHEALTH ADOPTION FRAMEWORK FOR
DEVELOPING COUNTRIES: A SYSTEMATIC REVIEW
https://aircconline.com/hiij/V10N3/10321hiij01.pdf
GENDER DISPARITYOF TUBERCULOSISBURDENIN LOW-AND MIDDLE-INCOME COUNTRIES: A SY...hiij
The tuberculosis burden is higher in the population from low- and middle-income countries (LMICs) and
differently affects gender. This review explored risk factors that determine gender disparity in tuberculosis
in LMICs. The research design was a systematic review. Three databases; Google Scholar, PubMed, and
HINARI provided 69 eligible papers.The synthesized data were coded, grouped and written in a descriptive
narrative style. HIV-TB co-infected women had a higher risk of mortality than TB-HIV-infected men. The
risk of Vitamin-D deficiency-induced tuberculosis was higher in women than in men. Lymph node TB,
breast TB, and cutaneous and abdominal TB occurred commonly in women whereas pleuritis, miliary TB,
meningeal TB, pleural TB and bone and joint TB were common in men. Employed men had higher contact
with tuberculosis patients and an increased chance of getting the disease. Migrant women were more likely
to develop tuberculosis than migrant men. The TB programmers and policymakers should balance the
different gaps of gender in TB-related activities and consider more appropriate approaches to be genderbased and have equal access to every TB-associated healthcare.
BRIEF COMMUNICATIONS DATA HYGIENE: IMPORTANT STEP IN DECISIONMAKING WITH IMPL...hiij
Medical and health data that have been entered into an electronic data system in real-time cannot be
assumed to be accurate and of high quality without verification. The adoption of the electronic health
record (EHR) by many countries to the support care and treatment of patients illustrates the importance of
high quality data that can be shared for efficient patient care and the operation of healthcare systems.
This brief communication provides a high-level overview of an EHR system and practices related to high
data quality and data hygiene that could contribute to the analysis and interpretation of EHR data for use
in patient care and healthcare system administration.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Leading Change strategies and insights for effective change management pdf 1.pdf
Predictive comparative qsar analysis of as 5 nitrofuran-2-yl derivatives myco bacterium tuberculosis h37 rv inhibitors
1. Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013
PREDICTIVE COMPARATIVE QSAR ANALYSIS OF
AS 5-NITROFURAN-2-YL DERIVATIVES MYCO
BACTERIUM TUBERCULOSIS H37RV
INHIBITORS
Doreswamy1 and Chanabasayya .M. Vastrad2
1
Department of Computer Science Mangalore University , Mangalagangotri-574 199, Karnataka,
INDIA
2
Department of Computer Science Mangalore University , Mangalagangotri-574 199, Karnataka,
INDIA
ABSTRACT
Antitubercular activity of 5-nitrofuran-2-yl Derivatives series were subjected to Quantitative Structure
Activity Relationship (QSAR) Analysis with an effort to derive and understand a correlation between the
biological activity as response variable and different molecular descriptors as independent variables.
QSAR models are built using 40 molecular descriptor dataset. Different statistical regression expressions
were got using Partial Least Squares (PLS) ,Multiple Linear Regression (MLR) and Principal Component
Regression (PCR) techniques. The among these technique, Partial Least Square Regression (PLS)
technique has shown very promising result as compared to MLR technique A QSAR model was build by a
training set of 30 molecules with correlation coefficient ( ݎଶ ) of 0.8484 , significant cross validated
ଶ
correlation coefficient ( ݍଶ ) is 0.0939, ݐݏ݁ݐ ܨis 48.5187, ݎଶ for external test set ( ) ݎ_݀݁ݎis -0.5604,
coefficient of correlation of predicted data set ( ݎ_݀݁ݎଶ )݁ݏis 0.7252 and degree of freedom is 26 by
Partial Least Squares Regression technique.
KEYWORDS
TB, MLR , PLS , PCR , LOO
1. INTRODUCTION
Tuberculosis in humans is generally caused by mycobacterium tuberculosis(TB). The desease is
spread by respirable droplets generated during effective expiratory manoeuvres such as coughing.
TB desease can be either active or latent[1] . The World Health Organization (WHO) asses that
within the next twenty years about thirty million people will be troubled with the bacillus [2-3].
The analytic management of TB has depends dully on a limited number of drugs such as
Isonicotinic acid, Hydrazide,Rifadin, Rimactane ,Myambutol ,Streptomycin, Ethionamide,
Pyrazinamide, Fluroquinolones etc [4]. Still with the origin of these special chemical drugs the
DOI: 10.5121/hiij.2013.2404
47
2. Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013
spread of TB has not been eradicated completely because of delayed treatment programmes
.There is now recognition that new drugs to treat TB are necessarily required, particularly for use
in shorter medication procedure than are possible with the current agents and which can be
engaged to treat multi-drug resistant and hidden disease[5].
5-nitrofuran-2-yl shows effective in vitro and in vivo antimycobacterial activity [6]. There is also
a great effort to find and develop newer, 5-nitrofuran-2-yl, and some of them might have value in
the remedy of TB [7]. Chemo informatics[26] and computer-aided drug design (CADD) are
likely to contribute to a possible solution for the dangerous situation regarding this infectious
disease by assisting in the swift identification of new effective anti-TB agents. The other way for
overcoming the absence of empirical analysis for biological systems is depends on the activity to
develop quantitative structure activity relationship (QSAR) [8] . QSAR models are mathematical
expressions formulating a relationship between chemical structures and biological activities.
These models have different capability, which is providing a deeper knowledge about the process
of biological activity. In the first step of a usual QSAR study one needs to find a set of molecular
descriptors with the higher influence on the biological activity of interest [9]. A broad scope of
molecular descriptors[10] has been used in QSAR modeling. These molecular descriptors[11]
have been categorised into different classes, including constitutional, geometrical, topological,
quantum chemical and so on. Using such an way one could predict the activities of newly
formulated compounds before a conclusion is being made whether these compounds should be
truly synthesized and tested. We examine the performance of Partial Least Squares(PLS) based
QSAR models with the results produced by Multi Linear Regression(MLR ) and Principal
Component Regression (PCR) methods to discover basic requirements for additional bettered
antitubercular activity.
2. MATERIALS AND METHODS
2.1 MOLECULAR DESCRIPTOR DATA SETS
A set of fourty molecule compounds relates to derivatives for mycobacterium TB(H37Rv)
inhibitors were taken from large antitubercular drug molecule databases[12] using substructure
mining tool Schrodinger Canvas 2010(Trial version) [13]. All molecules were handled by the
Vlife MDS [14] - 2D coordinates of atoms were recalculated counter ions and salts were
eliminated from molecular structures, molecules were neutralized, mesomerized and aromatized.
Data sets were then refined from duplicates. The 2D-QSAR models were produced using a
training set of thirty molecules. Predictive ability of the models was assessed by a test set of ten
molecules with consistently distributed biological activities. The observed selection of test set
molecules was made by seeing the fact that test set molecules shows a range of biological activity
similar to the training set. The actual and predicted biological activities of the training and test
set molecules are given in Table 1.
48
3. Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013
Sl
no
1
Compound
IC50a(µg/ml)
PIC50b
Obsr
Pred
Residual
4.41
5.3820
0.027
6.41
5.193
5.2282
0.0352
3.29
5.482
5.2282
0.2538
8.7
5.060
5.2282
0.1682
11.38
4.943
6.0161
1.0731
6.83
5.165
5.3820
0.217
2.53
5.596
5.3820
0.214
-4.35
5.361
5.3820
0.021
-0.95
H3 C
2
5.355
6.022
5.3820
0.64
-4.12
5.385
5.3820
0.003
-7.8
5.062
5.3820
0.32
CH 3
HN
O
O
3
4
H3 C
HN
O
O
5
6
7
H3 C
8
O
CH 3
S
Cl
N
O
O
HN
O
CH
9
O
O
O
S
N
H3 C
O
O
HN
H3C
O
O
CH
10
O
O
O
S
N
H3 C
O
O
HN
H3C
O
O
11
H3 C
O
O
CH 3
S
N
O
O
H3C
HN
O
49
4. Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013
12
-0.82
6.086
6.0867
0.0007
4.01
5.317
5.2282
0.0888
3.9
5.408
5.2282
0.1798
-5.44
5.264
5.2282
0.0358
0.15
6.823
5.3820
1.441
6.79
5.168
5.3820
0.214
9.26
5.033
5.3820
0.349
-3.15
5.501
5.3820
0.119
6.04
5.218
5.5359
0.3179
-3.47
5.459
5.3820
0.077
13
CH 3
O
O
CH 3
H3 C
S
O
N
O
O
HN
H
N
O
O
14
15
16
17
18
19
20
21
50
5. Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013
22
-4.23
5.373
5.3820
0.009
-2.48
5.605
5.3820
0.223
-1.65
5.782
5.3820
0.4
-3.62
5.441
5.6625
0.2215
6.2
5.207
5.3820
0.175
98.23
4.007
4.0090
0.002
-5.11
5.291
5.2282
0.0628
1.15
5.939
5.2282
0.7108
-6.59
5.181
5.2282
0.0472
-0.08
7.096
7.0986
0.0026
23
24
O
25
CH 3
O
O
NH
O
O
O
O
26
27
28
29
O
30
H2 N
N
N
O
O
N
31
H3 C
O
O
O
51
6. Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013
32
O
N
O
0.15
6.823
6.7122
0.1108
-2.55
5.593
5.3820
0.211
-1.05
5.978
5.0744
0.9036
-9.38
5.027
5.2282
0.2012
-4.8
5.318
5.0744
0.2436
10.73
4.969
5.0744
0.1054
6.25
5.204
5.2282
0.0242
4.26
5.370
5.2282
0.1418
-8.01
N
5.096
5.2282
0.1322
O
33
O
F
HN
O
34
35
H3 C
O
HS
N
Cl
Cl
HN
Cl
HN
O
O
36
37
38
CH 3
O
HN
O
O
39
40
Table 1 Molecular structure with Observed and Predicted activity of 5-nitrofuran-2-yl used in
training and test set using Model-1 (PLS)
Expt. = Experimental activity, Pred. = Predicted
activity IC50a = Compound concentration in micro mole required to inhibit growth by 50%
52
7. Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013
PIC50b = -Log (IC50 × 10ି): Training data set developed using model 1 (PLS) and Test set
is light blue shaded.
2.2 BIOLOGICAL OBSERVED ACTIVITY DATA
For the evolution of QSAR models of 5-nitrofuran-2-yl ,of processes antitubercular activity in
terms of half maximum inhibitory concentration IC50 (µM) versus (H37Rv) strains were took
from the antitubercular drug molecule databases[12]. The IC50 activity data contains only
molecules that have at least exhibited some activity. The biological activity data (IC50) were
transformed in to pIC50 according to the formula pIC50 = (−log (IC50 × 10ି)) was used as
response values, thus correlating the data linear to the free energy change.
2.3 DESCRIPTOR CALCULATION FOR MOLECULAR DATASET
The VLife MDS tool used for the computation of various molecular descriptors containing
topological index (J), connectivity index (x), radius of gyration (RG), moment of inertia, Wiener
index(W), balabian index(J), centric index, hosoya index (Z), information based indices, XlogP,
logP, hydrophobicity, elemental count, path count, chain count, pathcluster count, molecular
connectivity index (chi), kappa values, electro topological state indices, electrostatic surface
properties, dipole moment, polar surface area(PSA), alignment independent descriptor
(AI)[11,14. The calculated molecular descriptors were gathered in a data matrix. The
preprocessing for the generated molecular descriptors was done by removing invariable (constant
column) and cross-correlated descriptors (with r = 0.99). which happen in total 156, 125 and 162
molecular descriptors for MLR, PCR and PLS accordingly to be used for QSAR analysis.
2.4 CREATION OF TRAINING AND TEST SET
The dataset of forty molecular descriptors is split s into training and test set by Sphere Exclusion
(SE)[15-16] technique. In this technique initially data set splits into training and test set using
sphere exclusion technique. In this technique variance value provides an idea to handle training
and test set size. It needs to be adapted by trial and error until a desired split of training and test
set is acquired. Increase in dissimilarity value results in increase in number of molecules in the
test set. This technique is used for MLR, PCR and PLS models with pIC50 activity data as
response variable and various 2D molecular descriptors computed for the molecules as
independent variables.
2.5 MODEL VALIDATION
Model validation [17-18] is a essential manner of quantitative structure–activity relationship
(QSAR) modelling. This is done to test the internal stability and predictive capability of the
QSAR models. These three QSAR models were validated by the following method.
2.5.1 INTERNAL MODEL VALIDATION
Internal model validation was carried out using leave-one-out (LOO-Qଶ ) method. For calculating
qଶ , each sample in the training set was eliminated once and the activity of the eliminated sample
was predicted by using the model developed by the remaining samples. The Qଶ computed using
the expression which explains the internal strength of a model.
53
8. Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013
∑(ଢ଼
Qଶ = 1 − ∑(ଢ଼ ౦౨ౚ
ି ଢ଼ౘ౩ )మ
(1)
మ
ౘ౩ ି ଢ଼ౣ )
In Eq. (1), Y୮୰ୣୢ and Y୭ୠୱ indicate predicted and observed activity values accordingly and Y୫ୣୟ୬
signify mean activity value. A model is considered acceptable when the value of Qଶ exceeds 0.5.
2.5.2 EXTERNAL MODEL VALIDATION
External model validation, the activity of each sample in the test set was predicted using the
model created by the training set. The pred_r ଶ value is computed as follows.
pred_r ଶ =
∑(ଢ଼౦౨ౚ(౪౩౪) ି ଢ଼౪౩౪ )మ
∑(ଢ଼౪౨ ି ଢ଼ౣ(౪౨) )మ
(2)
In Eq (2) Y୮୰ୣୢ(୲ୣୱ୲) and Y୲ୣୱ୲ indicate predicted and observed activity values for the test set and
Y୲୰ୟ୧୬ indicates mean activity value of the training set. For the predictive QSAR model, the value
of pred_r ଶ must be more than 0.5.
2.5.3 RANDOMIZATION TEST
Randomization test or Y-scrambling is key mean of statistical validation. To assess the statistical
importance of the QSAR model for the dataset, one tail hypothesis testing is used. The strength
of the models for training sets was tested by examining these models to those derived for random
datasets. Random sets were produced by rearranging the activities of the samples in the training
set. The statistical model was determined using different randomly reorganize activities (random
sets) with the chosen molecular descriptors and the equivalent Qଶ were computed. The
importance of the models for that reason obtained was developed based on a computed Zୱୡ୭୰ୣ.
A Z score value is calculated by the following equation:
(୦ ି ஜ)
Zୱୡ୭୰ୣ =
(3)
Where ℎ is the Qଶ value computed for the dataset, µ the mean Qଶ , and is its σ standard deviation
calculated for various iterations using models build by different random datasets. The probability
(a) of importance of randomization test is derived by comparing Zୱୡ୭୰ୣ value with Zୱୡ୭୰ୣ critical
value as stated, if Zୱୡ୭୰ୣ value is less than 4.0; otherwise it is computed by the expression as
given in the literature. For example, a Zୱୡ୭୰ୣ value more than 3.10 proposes that there is a
probability (a) of smaller than 0.001 that the QSAR model build for the dataset is random. The
randomization test proposes that all the created models have a probability of less than 1% that the
model is produced by chance.
2.6 MULTIPLE LINEAR REGRESSION (MLR) ANALYSIS
MLR technique used for modelling linear relationship between a response variable Y (pIC50) and
independent variables X (2D molecular descriptors). MLR is based on least squares technique: the
model is fit such that sum-of-squares of differences of actual and a predicted values are
minimized. MLR estimates the regression coefficients ( r ଶ ) by applying least squares fitting
54
9. Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013
technique. The model builds a relationship in the form of a straight line (linear) that best
estimates all the individual data points. In regression analysis, conditional mean of response
variable (pIC50) Y depends on (molecular descriptors)X. MLR analysis add to this idea to include
more than one independent variables. Regression expression takes the form.
(4)
Y = bଵ xଵ + bଶ xଶ + bଷ xଷ + c
where Y is a response variable, ‘b’s are regression coefficients for corresponding ‘x’s are
molecular descriptors(independent variables), ‘c’ is a regression constant or intercept [19,25].
2.7 PRINCIPAL COMPONENT REGRESSION (PCR) ANALYSIS
Principal Component Regression (PCR) is a regression technique that uses principal component
analysis(PCA) when evaluating regression coefficients. PCR presents a technique for finding
structure in datasets. Its object is to group correlated variables, replacing the earlier descriptors
by new set called principal components (PCs). These PC’s are uncorrelated and are developed as
a simple linear aggregation of earlier variables. It moves the data into a new set of axes such that
first few axes indicates most of the variations within the data. First PC (PC1) is expressed in the
direction of maximum variance of the whole dataset. Second PC (PC2) is the direction that
defines the maximum variance in orthogonal subspace to PC1. Consequent components are taken
orthogonal to the particular formerly chosen and defines best of remaining variance, by locating
the data on new set of axes, it can points major fundamental structures certainly. Value of each
point, when moved to a given axis, is called the PC value. PCA chooses a new set of axes for the
data. These are chosen in decreasing order of variance within the data. The aim of principal
component PCR is the computation of values of a response variable on the basis of chosen PCs of
independent variables [21].
2.8 PARTIAL LEAST SQUARES (PLS) REGRESSION ANALYSIS
PLS is a well known regression technique which can be used to correlate one or more response
variable (Y) to various independent variables(X) . PLS relates a matrix Y of response variables to
a matrix X of molecular descriptors. PLS is useful in conditions where the number of molecular
descriptors( independent variables) exceeds the number of samples, when X data contain
colinearties or when N is less than 5M, where N is number of samples and M is number of
response variables. PLS builds orthogonal components using existing correlations between
independent variables and corresponding outputs while also keeping most of the variance of
independent variables. Major aim of PLS regression is to predict the activity (Y) from X and to
define their common frame[22,23] . PLS is probably the least contrary of various multivariate
extensions of MLR model. PLS is a technique for constructing predictive models when factors
are many and highly collinear.
2.9 EVALUATION OF THE QSAR MODELS
The created QSAR models are computed using the following statistical parameters: N (Number
of samples in regression); K (Number of independent variables(molecular descriptors)); DF
(Degree of freedom); optimum component ( number of optimums); r ଶ ( the squared correlation
coefficient); F test (Fischer’s Value) for statistical importance; qଶ (cross-validated correlation
55
10. Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013
coefficient); pred_r ଶ ( r ଶ for external test set); Zୱୡ୭୰ୣ ( Z score computed by the randomization
test); Best_ran_r ଶ (maximal r ଶ value in the randomization test) ; Best_ran_qଶ (maximal qଶ value
in the randomization test) ; α ( statistical importance parameter obtained by the randomization
test). The correlation coefficient r ଶ is a respective standard of fit by the regression expression. It
expressed the part of the variation in the observed data is analyzed by the regression. Despite, a
QSAR models are examined to be predictive, if the following prerequistes are satisfied: r ଶ > 0.6 ,
qଶ > 0.6 and pred_r ଶ > 0.5 [24] . The F-test indicates the ratio of variance described by the
model and variance due to the error in the regression. High values of the F-test indicate that
model is statistically meaningful. The reduced standard error of pred_r ଶ se , qଶ _se and r ଶ _se
demonstrates actual value of the fitness of the model. The cross-correlation extent was set at 0.5.
3. RESULTS
Taining set of 30 and 10 of test set of 5-nitrofuran-2-yl having different substitution were
employed.
3.1 CREATION OF QSAR MODELS
3.1.1 PARTIAL LEAST SQUARES (PLS) REGRESSION ANALYSIS
The molecular descriptors were applied to PLS technique to developQSAR models by using
simulated anealing variable selection mode. PLS model is having following QSAR Eq.(5) with
five descriptors.
pIC50 =
1.8704(StsCcount) + 4.0747(chi5chain) − 0.6865(SaaaCcount) + 0.7046(SssScount) −
0.1538 (SdssCcount) + 4.9478
(5)
Table 2 Statistical parameters of PLS, MLR And PCR
Parameters
N
DF
rଶ
PLS
40
26
0.8484
MLR
40
24
0.8484
PCR
40
28
0.3289
qଶ
F-test
best_ran_r ଶ
best_ran_qଶ
Zୱୡ୭୰ୣ_୰ୟ୬_୰ ଶ
Zୱୡ୭୰ୣ_୰ୟ୬_୯ ଶ
0.0939
48.5187
0.56429
−0.03892
3.43122
1.59111
0.0932
26.8725
0.28620
−0.08598
8.11471
1.31886
−5.3805
13.7231
0.32891
−2.93782
2.81258
−2.47533
α_ran_r ଶ
α_ran_q ଶ
0.00100
0.10000
0.00000
0.10000
0.01000
99.00000
r ଶ _se
0.2277
0.2370
0.4617
q _se
pred_r ଶ
0.5568
−0.5604
0.5797
−0.5616
1.4237
−0.0734
pred_r ଶ se
0.7252
0.7255
0.6015
ଶ
56
11. Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013
The above analysis directs to the improvement of statistically meaningful QSAR model, which
allows understanding of the molecular properties/features that play an key role in governing the
variation in the activities. In addition, this QSAR study allowed examining influence of very
simple and easy-to-compute molecular descriptors in discovering biological activities, which
could shed light on the important factors that may aid in design of new potent molecules.
All the parameters and their significance, which contributed to the specific
inhibitory activity in the generated model is discussed below.
antitubercular
1. StsCcount: This descriptor indicates the total number of carbon atoms with a triple bond and a
single bond exist in the molecule. Positive Contribution of this descriptor to the model is
31.72%.
2.chi5chain: This descriptor signifies a retention index for five membered ring. Positive
Contribution of this descriptor to the model is 21.99%.
3. SaaaCcount: This descriptor defines the total number of carbon connected with three aromatic
bonds. Negative Contribution of this descriptor to the model is -23.28%.
4. SssScount: This descriptor indicates the total number of sulphur atom attached with two
single bonds. Positive Contributions of this descriptor to the model is 11.95%.
5. SdssCcount: This descriptor defines the total number of carbon connected with one double
and two single bond. Negative Contribution of this descriptor to the model is -11.06%.
Figure 1 Observed vs. Predicted activities for training and test set molecular descriptors by Partial Least
Square model. (A) Training set (Red dots) (B) Test Set (Blue dots).
The PLS model gave correlation coefficient ( r ଶ ) of 0.8484, significant cross validated correlation
coefficient ( qଶ ) of 0.0939, F-test of 48.5187 and degree of freedom 26. The model is validated
by α_ran_r ଶ = 0.00100, α_ran_q ଶ = 0.10000, best_ran_r ଶ = 0.56429, best_ran_qଶ = -0.03892,
Zୱୡ୭୰ୣ_୰ୟ୬_୰ ଶ = 3.43122 and Zୱୡ୭୰ୣ_୰ୟ୬_୯ ଶ =1.59111. The randomization test proposes that the
created model have a probability of smaller than 1% that the model is build by chance. Statistical
57
12. Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013
data is presented in Table 2. The graph of observed vs. predicted activity is demonstrated in
Figure 1. The descriptors which contribute for the QSAR model is demonstrated in Figure 2.
Figure 2 Percentage contribution of each descriptor in created PLS model describing variation in the
activity
3.1.2 MULTIPLE LINEAR REGRESSION (MLR) ANALYSIS
The QSAR analysis by Multiple Linear Regression method with simulated annealing variable
selection technique, the final QSAR model is created having five descriptors is shown in Eq. (6).
pIC50 =
1.8681(± 0.2421)StsCcount + 4.0722(± 0.7599)chi5chain −
0.6879(± 0.1139)SaaaCcount + 0.7033(± 0.2393)SssScount −
0.1548 (± 0.0265)SdssCcount + 4.9497
(6)
MLR Model has a correlation coefficient ( r ଶ ) of 0.8484, significant cross validated correlation
coefficient ( qଶ ) of 0.0932, F test of 26.8725 and degree of freedom 24. The model is validated
by α_ran_r ଶ = 0.00000 , α_ran_q ଶ = 0.10000, best_ran_r ଶ = 0.28620, best_ran_qଶ =
−0.08598 , Zୱୡ୭୰ୣ_୰ୟ୬_୰ ଶ = 8.11471 andZୱୡ୭୰ୣ_୰ୟ୬_୯ ଶ = 1.31886
The randomization test proposes that the created model have a probability of smaller than 1%
that the model is build by chance. The observed and predicted values with residual values are
demonstrated in Table 1.Statistical data is demonstrated in Table 2.The graph of observed vs.
predicted activity demonstrated is in Figure 3. The descriptors which contribute for the QSAR
model are demonstrated in Figure 4. All the parameters and their significance, which contributed
to the specific antitubercular inhibitory activity in the generated models are explained below.
Figure 3 Observed vs. Predicted activities for training and test set molecular descriptors from the Multiple
Linear Regression model. (A) Training set (Red dots) (B) Test Set (Blue dots).
58
13. Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013
1. StsCcount: This descriptor indicates the total number of carbon atoms with a triple bond and a
single bond present in the molecule. Positive Contribution of this descriptor to the model is
31.67%.
2.chi5chain: This descriptor signifies a retention index for five membered ring. Positive
Contribution of this descriptor to the model is 21.97%.
3. SaaaCcount: This descriptor defines the total number of carbon connected with three aromatic
bonds. Negative Contribution of this descriptor to the model is -23.32%.
4. SssScount: This descriptor indicates the total number of sulphur atom attached with two
single bonds. Positive Contributions of this descriptor to the model is 11.92%.
5. SdssCcount: This descriptor defines the total number of carbon connected with one double
and two single bond. Negative Contribution of this descriptor to the model is -11.12%.
Figure 4 Percentage contribution of each descriptor in created MLR model describing variation in the
activity.
3.1.3 PRINCIPAL COMPONENT REGRESSION (PCR) ANALYSIS
The molecular descriptors were applied to under goes PCR technique to create QSAR model with
Simulated anealining variable selection mode by using PCR model. The final QSAR model is
Eq. (7) was created having one descriptor as follows.
pIC50 = 1.7397StsCcount + 5.3563
(7)
The PCR model gave correlation coefficient ( r ଶ ) is 0.3289, significant cross validated
correlation coefficient ( qଶ ) of -5.3805, F test of 13.7231 and degree of freedom 28. The model is
validated by α_ran_r ଶ = 0.01000, α_ran_q ଶ = 99.00000, best_ran_r ଶ = 0.32891, best_ran_qଶ
=-0.13938 , Zୱୡ୭୰ୣ_୰ୟ୬_୰ ଶ = 2.81258 and Zୱୡ୭୰ୣ_୰ୟ୬_୯ ଶ =-2.47533. The randomization test proposes
that the created model have a probability of smaller than 1% that the model is build by chance.
59
14. Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013
Statistical data is demonstrated in Table 2. The graph of observed vs. predicted activity is in
demonstrated Figure 5 .The descriptors which contribute for the QSAR model is demonstrated in
Figure 6.
Figure 5 Observed vs. Predicted activities for training and test set molecular descriptors by Principal
Component Regression model. A) Training set (Red dots) B) Test Set (Blue dots).
All the parameters and their significance, which contributed to the specific
inhibitory activity in the generated models are discussed here.
antitubercular
1. StsCcount: This descriptor indicates the total number of carbon atoms with a triple bond and a
single bond present in the molecule. Positive Contribution of this descriptor to the model is
100%.
Figure 6 Percentage contribution of each descriptor in developed PCR model describing variation in the
activity
60
15. Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013
4. CONCLUSION
The 2D QSAR analysis were conducted with a series of 5-nitrofuran-2-yl derivatives for
mycobacterium tuberculosis(H37Rv) inhibitors , and some useful predictive models were
obtained. The physicochemical molecular descriptors were found to have an key role in
governing the change in activity. The statistical parameters demonstrate the estimation power of
QSAR model for the molecular descriptor data set from which it has been determined and
evaluate it only internally. The overall performance of prediction was found to be around 84% in
case of PLS and MLR. Among the three 2D-QSAR models (MLR, PCR, and PLS), the results of
PLS and MLR analysis showed significant predictive power and reliability as compare to PCR
technique.
ACKNOWLEDGEMENTS
The Authors are thankful to Dr Mahesh .B. Palkar Department of Pharmaceutical Chemistry
K.L.E Pharmacy College Hubli
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]T
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
“Tuberculosis”, Centers for Disease Control and Prevention1600 Clifton Rd. Atlanta, GA 30333,
USA http://www.cdc.gov/tb/topic/basics/default.htm
“Weekly Epidemiological Record (WER)”,WHO annual report on global TB control – summary
http://www.who.int/wer/2003/wer7815/en/index.html
Phyllis C. Braun, PhD and John D. Zoidis, MD, “Update on Drug-Resistant Pathogens: Mechanisms
of Resistance, Emerging Strains”, http://www.rtmagazine.com/issues/articles/2004-01_01.asp
“Tuberculosis
management”,
From
Wikipedia,
the
free
encyclopaedia
http://en.wikipedia.org/wiki/Tuberculosis_management
”Multidrug-resistant tuberculosis (MDR-TB)” , From World Health Organization
http://www.who.int/tb/challenges/mdr/en/
awari NR, Bairwa R, Ray MK, Rajan MG, Degani MS. Bioorg Med Chem Lett. (2010 ),“Design,
synthesis,and biological evaluation of 4-(5-nitrofuran-2-yl)prop-2-en-1-one derivatives as potent
antitubercular agents.” ,1;20(21):6175-8. doi: 10.1016/j.bmcl.2010.08.127. Epub 2010 Sep 16.
Sriram D, Yogeeswari P, Dhakla P, Senthilkumar P, Banerjee D, Manjashetty TH (2009),“5Nitrofuran-2-ylderivatives: synthesis and inhibitory activities against growing and dormant
mycobacterium species.” , Bioorganic & medicinal chemistry letters 19:4 pg 1152-4
R. Karbakhsh1,* and R. Sabet (2011),“Application of different chemometric tools in QSAR study of
azoloadamantanes against influenza A virus”, Research in Pharmaceutical Sciences; 6(1): 23-33
“Molecular
Descriptors“
,The
Free
Online
resource
http://www.moleculardescriptors.eu/tutorials/what_is.htm
“Molecular Descriptors Guide” , Version 1.0.2 Copyright [2008] US Environamental Protection
agency
“Streamline Drug Discovery with CDD colabrative web based software “ ,
https://www.collaborativedrug.com/ ( Accesed in May-june [2012] )
”Canv
as”,
A
comprehensive
cheminformatics
computing
environment
http://www.schrodinger.com/products/14/23/
“VlifeMDS”,
Integrated
platform
for
Computer
Aided
Drug
Design
(CADD)
http://www.vlifesciences.com/products/VLifeMDS/Product_VLifeMDS.php
“Sphere Exclusion Method for set selection” ,Rajarshi Guha Penn State University
http://rguha.net/writing/pres/tropsha.pdf
61
16. Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013
[15] Mary Ann Liebert,(1999), “Dissimilarity-Based Algorithms for Selecting Structurally Diverse Sets of
Compounds” , JOURNAL OF COMPUTATIONAL BIOLOGY Volume 6, Numbers 3/4, Inc. Pp.
447–457
[16] Tropsha, A.; Gramatica, P.; Gombar,V.K.(2003),”The importance of being earnest: Validation is the
absolute essential for successful application and interpretation of QSPR models.”, QSAR Comb. Sci. ,
22, 69-77
[17] Partha Pratim Roy, Somnath Paul, Indrani Mitra and Kunal Roy(2009) ,“On Two Novel Parameters
for Validation of Predictive QSAR Models” , Molecules , 14, 1660-1701 ISSN 1420-3049
[18] “Multile linear Regression”, http://www.ltrr.arizona.edu/~dmeko/notes_11.pdf
[19] Dr. Frank Dieterle ,“Variable Selection by Simulated Annealing”,
http://www.frankdieterle.de/phd/2_8_6.html
[20] Hwang , Dan Nettleton ,“Principal Components Regression With Data-Choosen Components and
related methods” , J.T. Gene www.math.cornell.edu/~hwang/pcr.pdf
[21] Herv´e Abdi1 ,“Partial Least Squares(PLS) Regression ” ,The University of Texas at Dallas
[22] Randall D. Tobias, “An introduction to partial least squares Regression”, SAS Institute Inc., Carry,
NC www.ats.ucla.edu/stat/sas/library/pls.pdf
[23] Golbraikh .A , and A. Tropsha, (2002),”Predictive QSAR modeing based on diversity of sampling
of experimental datasets for the training and test set selection “, J. Comp Aided . Mol Design ,
16:357-366.
[24] “Influence of observations on the misclassification probability in quadratic discriminant analysis”
,https://lirias.kuleuven.be/bitstream/123456789/85608/1/qda.pdf
[25] Craig A. James, “An introduction to the Computer Science and Chemistry of Chemical Information
Systems” eMolecules, Inc. http://www.emolecules.com/doc/cheminformatics-101.php
Authors
Doreswamy received B.Sc degree in Computer Science and M.Sc Degree in
Computer Science from University of Mysore in 1993 and 1995 respectively. Ph.D
degree in Computer Science from Mangalore University in the year 2007. After
completion of his Post-Graduation Degree, he subsequently joined and served
asLecturer in Computer Science at St. Joseph’s College, Bangalore from 1996
1999.Then he has elevated to the position Reader in Computer Science at Mangalor
Universityin year 2003. He was the Chairman of the Department of Post-Graduate Studies and research in
computer science from 2003-2005 and from 2009-2008 and served at varies capacitiesin Mangalore
University at present he is the Chairman of Board of Studies and Professor in Computer Science of
Mangalore University. His areas of Research interests include Data Mining and Knowledge Discovery,
Artificial Intelligence and Expert Systems, Bioinformatics ,Molecular modelling and simulation
,Computational Intelligence ,Nanotechnology, Image Processing and Pattern recognition. He has been
granted a Major Research project entitled “Scientific Knowledge Discovery Systems (SKDS) for
Advanced Engineering Materials Design Applications” from the funding agency University Grant
Commission, New Delhi , India. He has been published about 30 contributed peer reviewed Papers at
national/International Journal and Conferences. He received SHIKSHA RATTAN PURASKAR for his
outstanding achievements in the year 2009 and RASTRIYA VIDYA SARASWATHI AWARD for
outstanding achievement in chosen field of activity in the year 2010.
Chanabasayya.M.Vastrad received B.E. degree and M.Tech. degree in the year
2001and 2006 respectively. Currently working towards his Ph.D Degree in Computer
Scienceand Technology under the guidance of Dr. Doreswamy in the Department of
Post-Graduate Studies and Research in Computer Science , Mangalore University.
62