Deep analysis of chemical patents and Markush claims
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This document discusses the importance of Markush structures in patents and describes a technology for analyzing Markush claims in chemical patents. It notes that around 12% of US patents contain Markush claims covering thousands of structures. Existing Markush databases and services have limitations. The described Markush technology can represent complex Markush structures, search patent databases, visualize hits and non-hits, and assist with drafting new Markush claims. An example workflow uses the technology to extract Markush claims from relevant patents, validate structures against a project's ideas, and integrate the information into an in-house database. The technology aims to help write better patents and integrate IP knowledge into early-stage discovery.
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Cheminformatics approaches to support chemical identification delivered via t...
Cheminformatics approaches to support chemical identification delivered via t...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
The identification of chemicals in environment media depends on the application of analytical methods, the primary approach being one of the multiple mass spectrometry techniques. Cheminformatics solutions are critical to supporting the chemical identification process. This includes the assembly of large chemical substance databases, prioritization ranking of potential candidate search hits, and search approaches that support both targeted and non-targeted screening approaches. The US Environmental Protection Agency CompTox Chemicals Dashboard is a web-based application providing access to data for over 760,000 chemical substances. This includes access to physicochemical property, environmental fate and transport data, both human and ecological toxicity data, information regarding chemicals contained in products in commerce, and in vitro bioactivity data. Searches are allowed based on chemical identifiers, product and use, genes and assays associated with the EPA ToxCast assays and, specific to supporting mass spectrometry, searches based on masses and formulae. These searches make use of a novel “MS-Ready structures” approach collapsing chemicals related as mixtures, salts, stereoforms and isotopomers. The dashboard supports both singleton or batch searching by accurate mass/chemical formula, supported by MS-ready structures, and utilizes rich meta data to facilitate candidate ranking and the prioritization of chemicals of concern based on toxicity and exposure data. The dashboard also hosts tens of chemical lists that have been assembled from public databases, many supporting non-targeted analysis and mass spectrometry databases.
This presentation will provide an overview of the dashboard and will review our latest research into structure identification by searching experimental mass spectrometry data against predicted fragmentation spectra for LC-MS (positive and negative ion mode) and GC-MS (EI), a total of 3 million predicted spectra. We will also provide an overview of our progress supporting structure and substructure searching, using mass and formula-based filtering, and report on the latest applications of the dashboard to support structure identification projects of interest to the EPA. This abstract does not necessarily represent the views or policies of the U.S. Environmental Protection Agency.Introduction to Cheminformatics: Accessing data through the CompTox Chemicals...
Introduction to Cheminformatics: Accessing data through the CompTox Chemicals...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
This document provides an overview of the CompTox Chemicals Dashboard, which is a centralized hub for environmental chemical data developed by the US EPA. The dashboard contains data on over 880,000 chemical substances, integrating property data, hazard data, exposure data, and in vitro bioactivity data. It allows users to search for chemicals, obtain experimental and predicted data, perform batch searches on lists of chemicals, and access real-time prediction tools and links to external data sources. The dashboard is built on a cheminformatics foundation and aims to improve the efficiency of chemical risk assessment through consolidated access to relevant chemical information.Machine Learned Relevance at A Large Scale Search Engine
The document discusses machine learned relevance at a large scale search engine. It provides biographies of the two authors who have extensive experience in machine learning and search engines. It then outlines the topics to be covered, including an introduction to machine learned ranking for search, relevance evaluation methodologies, data collection and metrics, the Quixey search engine system, model training approaches, and conclusions.
Structure standardization approaches for mass spectrometry data integration
Structure standardization approaches for mass spectrometry data integrationUS Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
Structure standardization approaches for mass spectrometry data integration
Antony J. Williams, Charles Lowe, Gabriel Sinclair, Todd Martin and Valery Tkachenko
1 Center for Computational Toxicology & Exposure, Office of Research & Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
2Oak Ridge Institute for Science and Education (ORISE) Research Participant hosted by EPA. Office of Water, U.S. Environmental Protection Agency, 1200 Pennsylvania Avenue, N.W. Washington, DC 20460, USA.
3 Science Data Experts, 14909 Forest Landing Circle, Rockville, MD, USA
Chemical database searching is a key component of the identification of “known-unknowns” using high-resolution mass spectrometry (HRMS), especially the availability of metadata for the ranking of candidate hits returned from either mass or formula searches. The form of a chemical structure observed spectroscopically does not always match the form(s) stored in a database. For example, the database may host chemical substances in their salt form, or as complex mixtures, whereas mass spectrometry will detect the neutral form, or only one component of the mixture. The US Environmental Protection Agency (EPA) has adopted the approach of standardizing chemical structures into “MS-Ready” forms to link structures observed via mass spectrometry to all related form(s) within our chemical registration database and providing access to these data via the CompTox Chemicals Dashboard. The adoption of MS-Ready structures is now a staple of our structure identification workflows, especially for non-targeted analysis.
However, examination of the MS-Ready structures generated from our initially developed workflows indicated that we required more control of the structure standardization transformations in order to allow us to further test and optimize the associated rules. This necessitated the development of a web-based tool for us to optimize the rules set. This presentation will provide an overview of the MS-Ready structures approach, its value to our non-targeted analysis mass spectrometry workflows, and the web-based tool that has been developed to assist in processing, review and optimization of the standardization rules. This abstract does not necessarily represent the views or policies of the U.S. Environmental Protection Agency.
ICIC 2014 New Product Introduction ProQuest
ProQuest Dialog is a new search platform that provides:
1) Access to over 40 patent databases, 60 engineering databases, and other sources covering topics such as biomedical research, news, and trade.
2) Powerful search tools for users of all experience levels, from basic keyword searches to advanced syntax, with features like auto-complete and spelling correction.
3) Flexible pricing options so customers only pay for the content they access.
Consensus ranking and fragmentation prediction for identification of unknowns...
Consensus ranking and fragmentation prediction for identification of unknowns...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
High resolution mass spectrometry (HRMS) and non-targeted analysis (NTA) are advancing the identification of emerging contaminants in environmental and agricultural matrices. However, confidence in structure identification of unknowns in NTA presents challenges to analytical chemists. Structure identification requires integration of complementary data types such as reference databases, fragmentation prediction tools, and retention time prediction models. The goal of this research is to optimize and implement structure identification functionality within the US EPA’s CompTox Chemicals Dashboard, an open chemistry resource and web application containing data for ~760,000 substances. Rank-ordering the number of sources associated with chemical records within the Dashboard (Data Source Ranking) improves the identification of unknowns by bringing the most likely candidate structures to the top of a search results list. Incorporating additional data streams contained within the database underlying the Dashboard further enhances identifications. Integrating tandem mass spectrometry data into NTA workflows enables spectral match scores and increases confidence in structural assignments. We have generated and stored predicted MS/MS fragmentation spectra for the entirety of the Chemistry Dashboard using the in silico prediction tool CFM-ID. Predicted fragments incorporated into the identification workflow were used as both a scoring term and as a candidate threshold cutoff. Combining these steps within an open chemistry resource provides a freely available software tool for structure identification and NTA. This abstract does not necessarily represent the views or policies of the U.S. Environmental Protection Agency.Marrying ACDLabs technologies to eScience Projects at the Royal Society of C...
Marrying ACDLabs technologies to eScience Projects at the Royal Society of C...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
The Royal Society of Chemistry is one of the worlds foremost scientific societies, a primary publisher for the chemical sciences and an innovator in the domain of eScience. In order to deliver on a number of our eScience projects we utilize a number of components of Advanced Chemistry Development software including nomenclature, physchem prediction, spectroscopy tools and the ACD/Ilab web-based system. This presentation will provide an overview of a number of RSC projects where ACS/Labs software has played an important role in the delivery of the systems including ChemSpider and the National Chemical Database Service for the United Kingdom. We will also provide an overview of our vision to deliver a repository for various types of experimental chemistry data and how we foresee utilizing various prediction and validation software approaches to characterize the data as well as the potential to generate predictive models from the data. This couples directly with our intention to data enable our publication archive of over 300,000 articles extracting chemicals, reactions and analytical data from the historical records. Requirements engineering scenario based software requirement specification
Requirements Engineering – Writing the Software Requirements Specification (SRS). A CRI Group Workshop. The requirements engineering approach employed successfully in the EHR4CR process is shown and discussed in order to extract lessons learned and to use it for new projects.
Requirements engineering is the process of eliciting stakeholder needs and desires and develope them into an agreed set of detailed requirements. It serves as basis for all subsequent software development activities.
In general, a project begins with the requirement acquisition phase and ends with the specification of requirements in form of the Software Requirement Specification (SRS). Requirements specification may even be used to manage the consistency of the entire system.
Learning from the Requirements Engineering process in the the EU project EHR4CR. Especially the topics of Requirements Scenarios in the process of requirement gathering and the iterative writing and validation of software requirements specification (SRS) document can be applied to new projects. The Requirements Process consists of 4 steps: Requirements Elicitation – the art to receive meaningful requirements. Requirements Analysis – iterative improvement of quality of requirements. Writing the Requirements Specification document (Software Requirement Specification) and Requirements Validation - this is also done iteratively with several workshops.
Novel is the introduction of an iterative process for requirements engineering. Start with only a subset of software requirements, iterate the collection and validation until the full system is implemented. In each iteration, design modifications are made and new functional capabilities are added. Following tools for requirements gathering were used: Use Cases, Descriptions of current situation and workflow, Context diagram, Stakeholder interviews, Scenarios and Use Case workshops.
A novel scenario based approach for requirements engineering is being introduced: The domain scenario is used to estimate probable effects (situation analysis and long-range planning). The domain scenario is broken down into high-level "Usage Scenarios". Usage Scenarios describe critical business interactions and their anticipated operations; they serve as context for the use cases and the generation of requirements; they make sure requirements are complete.
Development of the SRS with involvement of scenarios: 1. Begin with Domain Scenarios; 2. Development of Usage Scenarios; 3. Software Requirements Specification document. Several round of change management were employed during writing the SRS. This possibility for correction and improvement ensured that the requirements are of high quality and applicability.
Software Engineering Research: Leading a Double-Agent Life.
The document discusses testing of closed-loop controllers in automotive systems. It notes the increasing complexity of automotive software and challenges in model-in-the-loop (MIL), software-in-the-loop (SIL), and hardware-in-the-loop (HIL) testing. It presents an approach to generate test cases for continuous controllers at the MIL level by representing requirements as objective functions and using a search-based approach to find worst-case scenarios. Experimental results found significantly worse scenarios than their industry partner, allowing them to generate better stress tests for HIL. The approach addresses a problem largely ignored in MIL testing of continuous controllers.
ICIC 2013 Conference Proceedings Jan Baur STN
The concept of the new STN platform
Jan Baur (FIZ Karlsruhe, Germany)
A new STN platform is being designed and developed to turn the needs and priorities of today's patent experts into a state-of-the-art search and analysis system that works the way users do.
This presentation describes how the project-oriented workflow concept of the new STN leads to improved workflow continuity and efficiency, and how the new platform allows users to interact simultaneously with their queries and search results. A variety of innovative functional aspects of the new STN that improve efficiency, such as instant analysis and refinement capabilities, and vastly increased power for text and chemical structure searches, will be showcased.
RecSys 2015 Tutorial - Scalable Recommender Systems: Where Machine Learning m...
RecSys 2015 Tutorial - Scalable Recommender Systems: Where Machine Learning m...Joaquin Delgado PhD.
This tutorial gives an overview of how search engines and machine learning techniques can be tightly coupled to address the need for building scalable recommender or other prediction based systems. Typically, most of them architect retrieval and prediction in two phases. In Phase I, a search engine returns the top-k results based on constraints expressed as a query. In Phase II, the top-k results are re-ranked in another system according to an optimization function that uses a supervised trained model. However this approach presents several issues, such as the possibility of returning sub-optimal results due to the top-k limits during query, as well as the prescence of some inefficiencies in the system due to the decoupling of retrieval and ranking.
To address this issue the authors created ML-Scoring, an open source framework that tightly integrates machine learning models into Elasticsearch, a popular search engine. ML-Scoring replaces the default information retrieval ranking function with a custom supervised model that is trained through Spark, Weka, or R that is loaded as a plugin in Elasticsearch. This tutorial will not only review basic methods in information retrieval and machine learning, but it will also walk through practical examples from loading a dataset into Elasticsearch to training a model in Spark, Weka, or R, to creating the ML-Scoring plugin for Elasticsearch. No prior experience is required in any system listed (Elasticsearch, Spark, Weka, R), though some programming experience is recommended. RecSys 2015 Tutorial – Scalable Recommender Systems: Where Machine Learning...
Search engines have focused on solving the document retrieval problem, so their scoring functions do not handle naturally non-traditional IR data types, such as numerical or categorical. Therefore, on domains beyond traditional search, scores representing strengths of associations or matches may vary widely. As such, the original model doesn’t suffice, so relevance ranking is performed as a two-phase approach with 1) regular search 2) external model to re-rank the filtered items. Metrics such as click-through and conversion rates are associated with the users’ response to items served. The predicted selection rates that arise in real-time can be critical for optimal matching. For example, in recommender systems, predicted performance of a recommended item in a given context, also called response prediction, is often used in determining a set of recommendations to serve in relation to a given serving opportunity. Similar techniques are used in the advertising domain. To address this issue the authors have created ML-Scoring, an open source framework that tightly integrates machine learning models into a popular search engine (SOLR/Elasticsearch), replacing the default IR-based ranking function. A custom model is trained through either Weka or Spark and it is loaded as a plugin used at query time to compute custom scores.
FAIRSpectra - Enabling the FAIRification of Analytical Science
Presentation at the BioFAIR Roadshow in Manchester.
23 April 2024
https://biofair.uk/
https://fairspectra.net
SciFinder and its utility in Drug discovery
SciFinder Scholar® is a Z39.50 Windows-based interface that provides easy access to the rich and diverse scientific information contained in the CAS databases including Chemical Abstracts from 1907 onwards. SFS is an elegant search interface to six core chemical-related databases. Five of these databases are produced by CAS itself
Accessing Environmental Chemistry Data via Data Dashboards and Applications t...
Accessing Environmental Chemistry Data via Data Dashboards and Applications t...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
The document summarizes a presentation about accessing environmental chemistry data through data dashboards and applications to mass spectrometry non-targeted analysis. It introduces the CompTox Chemicals Dashboard, which provides data on over 1.2 million chemicals. It describes how the dashboard supports mass spectrometry analysis and efforts to build databases of analytical methods and experimental spectra to help with identification. Sources of data and ongoing work to curate additional documents and spectra are also summarized.Similar to Deep analysis of chemical patents and Markush claims (20)
Utilizing Online Databases for the Purpose of Structure Identification – Appr...
Utilizing Online Databases for the Purpose of Structure Identification – Appr...
eScience at the Royal Society of Chemistry and our current initiatives
eScience at the Royal Society of Chemistry and our current initiatives
AIAA Conference - Big Data Session_ Final - Jan 2016
AIAA Conference - Big Data Session_ Final - Jan 2016
ChemSpider – disseminating data and enabling an abundance of chemistry platforms
ChemSpider – disseminating data and enabling an abundance of chemistry platforms
Cheminformatics approaches to support chemical identification delivered via t...
Cheminformatics approaches to support chemical identification delivered via t...
Introduction to Cheminformatics: Accessing data through the CompTox Chemicals...
Introduction to Cheminformatics: Accessing data through the CompTox Chemicals...
Machine Learned Relevance at A Large Scale Search Engine
Machine Learned Relevance at A Large Scale Search Engine
Structure standardization approaches for mass spectrometry data integration
Structure standardization approaches for mass spectrometry data integration
Consensus ranking and fragmentation prediction for identification of unknowns...
Consensus ranking and fragmentation prediction for identification of unknowns...
Marrying ACDLabs technologies to eScience Projects at the Royal Society of C...
Marrying ACDLabs technologies to eScience Projects at the Royal Society of C...
Requirements engineering scenario based software requirement specification
Requirements engineering scenario based software requirement specification
Software Engineering Research: Leading a Double-Agent Life.
Software Engineering Research: Leading a Double-Agent Life.
RecSys 2015 Tutorial - Scalable Recommender Systems: Where Machine Learning m...
RecSys 2015 Tutorial - Scalable Recommender Systems: Where Machine Learning m...
RecSys 2015 Tutorial – Scalable Recommender Systems: Where Machine Learning...
RecSys 2015 Tutorial – Scalable Recommender Systems: Where Machine Learning...
FAIRSpectra - Enabling the FAIRification of Analytical Science
FAIRSpectra - Enabling the FAIRification of Analytical Science
Accessing Environmental Chemistry Data via Data Dashboards and Applications t...
Accessing Environmental Chemistry Data via Data Dashboards and Applications t...
More from ChemAxon
Akos Tarcsay (ChemAxon): How fast is Chemaxon RDBMS Search?
Ákos' study aims to provide an overview of ChemAxon's different search engines and web services. A benchmark will be presented along with statistics on the performance of the different JChem engines.
Chemaxon EU UGM 2022 | Translating data to predictive models
Biological, chemical, and physical properties of molecules are encoded in their molecular structure. The challenge lies in discovering the relationships between the structure of the molecular graphs and the measured activity. In this presentation, we introduce Chemaxon’s new product, the Trainer Engine. It is designed to streamline the workflow starting from input data containing measured activities until validated models are implemented for a wide audience.
In addition to summarizing our results obtained with various machine learning model training scenarios, our goal is to highlight the model inference aspects. Accordingly, we present an integration use case with Chemaxon’s Design Hub. Connecting these applications widens the range of information resources available for decision-making on compound series to enhance drug discovery pipelines.
Translating data to predictive models
Biological, chemical and physical properties of molecules are encoded in their molecular structure. The challenge lies in discovering the relationships between the molecular graphs and the measured activity. Where data is measured, collected and curated for a series of compounds there is an opportunity to find the hidden relationships.
Chemical structures come in various shapes and sizes, depending on the scientists or even algorithms that create them. Though variability may sometimes seem subtle to a trained chemist’s eyes, these can introduce inconsistencies that impair chemical search algorithms or model building. Structure normalization is a key component of any cheminformatics workflow with an often underestimated significance. Finding relationships between chemical structures and their measured properties primarily relies on the representation of the chemical matter. Variability of the calculated features and descriptors for these representations can influence data analysis and accuracy of the predictions. During the first part of the presentation we will present the effect of chemical normalization on investigating correlations and building predictive models.
The second part of the talk will incorporate the results of model building on 163 ChEMBL targets extracted from the bioactivity benchmark set1. Results with different descriptor generation methods including ECFP fingerprints, MACCS key, structural properties, geometry properties and phy-chem properties will be discussed in detail. This part focuses on summarizing the results of more than 3000 Random Forest models.
Finally model development for ADMET targets will be highlighted including hERG cardiotoxicity prediction, permeability and blood brain barrier penetration. We will describe how these models can be built, analyzed, optimized and deployed using our new machine learning platform.
Efficient biomolecular structural data handling and analysis - Webinar with D...
In this joint event our experts are coming together to elaborate on the technical and scientific opportunities coming from this partnership. If you are working on the discovery of next generation drugs and using structure-based approach in your research, join us to learn how to leverage vast biomolecular structural data with innovative technologies.
Biomolecule structural data management
Chemaxon is a software and services provider founded in 1998 with headquarters in Budapest and around 250 employees worldwide. Their software supports drug discovery workflows from target identification through clinical trials. They provide tools for biomolecule data management to help with challenges like understanding biological context across teams and preventing data silos. Their software allows users to model, register, standardize, visualize, and design complex biomolecules like peptides, oligonucleotides, conjugates, proteins and antibodies to facilitate collaboration in biotherapeutic fields.
Cheminfo Stories 2021 | Virtual UGM | Marvin Pro: The first release
Marvin Pro is a new web-based chemical editor capable of drawing large synthesis schemes and biomolecules in publication quality. It can be extended with other modules and integrated with ChemAxon and third-party products. Some key features include impeccable visual quality matching journal styles, fast drawing using keyboard shortcuts, and chemical understanding for clean 2D structures and properties display. An alpha version is available on the Chemicalize website and the developers welcome feedback to help perfect the tool.
Enhanced stereochemistry representation
This document discusses enhanced stereochemistry representation in molecular structures, including absolute, and, and or labels. It explains that absolute labels indicate known exact stereochemistry, and labels indicate mixtures. Or labels signify a pure but unknown structure, while and labels represent mixtures where both configurations are present. Examples are given to illustrate how these labels can be used to define stereochemical mixtures and unknown structures. The document also demonstrates how chromatographic separation can be used to separate stereoisomer mixtures into pure components.
Intellectual property (IP) intelligence solutions designed for the way resear...
Leveraging IP intelligence through the researcher workflow requires the curation of chemistry patents including many thousands of molecules. This complex task is time-consuming and error-prone when done manually, whereas using ChemAxon’s ChemCuratora to analyze and extract chemical information in patents and other documents means the process can be done accurately in a fraction of the time.
GPS for Chemical Space - Digital Assistants to Support Molecule Design - Chem...
Boehringer Ingelheim's Nils Weskamp discusses eDesign: a computational platform for molecule design and optimization. This presentation explaing how to combine data, algorithms and user experience to impact compound design, and gives a glimpse into the agile and interdisciplinary teamwork as facilitated by Design Hub as a success factor for the development of digital tools.
Patent Data for Artificial Intelligence based Drug Discovery
Han-Jo KIm from Standigm presents on using ChemAxon's ChemCurator in processing structures and relevant data from patents, from Google Patents, PDF and text format.
Cheminfo Stories APAC 2020 - Chemical Descriptors & Standardizers for Machine...
This document discusses chemical descriptors and standardizers that are useful for machine learning models. It introduces standardizers, which canonicalize chemical structures for comparability, and structure checkers, which detect errors. Extended connectivity fingerprints (ECFP) are described as circular fingerprints that encode molecular structure. Case studies demonstrate using ECFP descriptors and standardizers to train deep neural networks for hERG activity prediction, achieving over 80% accuracy. Combining ECFP with topological descriptors led to slightly better performance than ECFP alone in a random forest model. In summary, customizable fingerprints and standardizers allow application in different tasks, and combining fingerprints with other descriptors can increase model performance.
Research data management on the cloud
The Synergy platform is ChemAxon’s approach to SaaS solutions for chemistry related R&D data management. It provides an integrated system that cleans, organizes and links together pre-clinical research data and a collaborative workspace where people from multiple sites can work with each other, as well as with CROs and partners. Besides giving a summary of the fundamental platform components, the presentation guides the audience through the process of capturing chemical data in our Compound Registration tool, uploading and standardizing assay results and visualizing, as well as analyzing combined chemical data and biological results.
Cheminfo Stories APAC 2020 - Introducing Design Hub & Compound Registration
This webinar will guide you through the Design Hub platform for scientific design and discovery project management using an antiviral compound optimization example. The workflow starts from capturing the first observation and corresponding hypothesis, showcase compound design relying on a vast amount of information sources and predictive models, including the new hERG toxicity prediction and docking using RDock. It will highlight how tracking the fate of compound ideas created as draft virtual compounds through synthesis targets and finally registered samples is fluently managed by the interaction of Design Hub and Compound Registration system.
Cheminfo Stories APAC 2020 - JChem Engines introduction
This document introduces ChemAxon's second generation JChem search engines, including the JChem PostgreSQL Cartridge and JChem Choral engine. It summarizes the key advantages like improved performance on large databases, enhanced tautomer matching, and new features. It also outlines how the different search products like cartridges and microservices are suited for different use cases and environments.
Cheminfo Stories APAC 2020 - Database management on desktop with JChem for Of...
This document provides an overview and summary of key features for JChem for Office and Instant JChem (IJC). JChem for Office allows embedding of live chemical structures in Microsoft Office files and includes features like chemical searching, properties calculations, and virtual library enumeration. IJC is a versatile data management framework that allows users to create, explore, share, and analyze chemical data through a project explorer interface, query builder, and grid/form views of data. It supports both structural and non-structural data management and analysis.
JChem Microservices
JChem Microservices provide microservices in small separate modules for different areas of ChemAxon functionalities like chemical dataset searching, conversion between chemical file formats.
Migration from joc to jpc or choral
This document outlines the steps for migrating from the JChem Oracle Cartridge (JOC) to the JChem PostgreSQL Cartridge (JPC) or Choral. It discusses the reasons for migration, strategies for large ecosystems, non-cartridge migration steps, and cartridge-specific data and code migration steps. Key points include moving to new features, performance gains, compatibility with PostgreSQL and Amazon RDS, and references for more information.
ChemAxon's Compliance Checker - Cheminfo Stories 2020 Day 5
This document discusses Compliance Checker, a SaaS tool for compound screening and subscriptions. It offers different subscription tiers (Lite, Standard) with varying features - such as number of users, compounds screened per second, authentication method, and data storage. The Standard subscriptions include Basic, Medium, Professional and Premium options that provide different levels of support, performance, high availability, and license scope. The Lite subscription can be monthly, quarterly or annual based on the number of compounds that can be screened.
Chemicalize Pro - Cheminfo Stories 2020 Day 5
Chemicalize is an online SaaS product providing UI based chemical calculations, drawing and searching as well as API based endpoints for integrators and embeddable web components for website owners. In the presentation we introduce the service and showing the essence of the embeddable web components through a real-life use case focusing on the compliance questions.
Pasteur Institute User Story - Cheminfo Stories 2020 Day 5
Here, we present an updated version of iPPI-DB, our manually curated database of PPI modulators. In this release, the data model, the graphical interface and the tools to query the database have been completely redesigned. We used Chemaxon MarvinJS and JChem library to support this development. We added new PPI modulators, new PPI targets, and extended our focus to stabilizers of PPIs as well. Finally, we introduce a web application relying on crowdsourcing for the maintenance of the database. This application can be used outside of our group to collaboratively maintain iPPI-DB within a community of curators.
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Efficient biomolecular structural data handling and analysis - Webinar with D...
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学校原件一模一样【微信：741003700 】《(salfor毕业证书)索尔福德大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
The debris of the ‘last major merger’ is dynamically young
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
(June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...
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Targeting Hsp90 and its pathogen Orthologs with Tethered Inhibitors as a Diagnostic and Therapeutic Strategy for cancer and infectious diseases with Dr. Timothy Haystead.THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...
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hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skillsDescribing and Interpreting an Immersive Learning Case with the Immersion Cub...
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
waterlessdyeingtechnolgyusing carbon dioxide chemicalspdf
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
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InIndia-post-harvestlosses-unscientificstorage,insects,rodents,micro-organismsetc.,accountforabout10percentoftotalfoodgrains
Graininfestation
Directdamage
Indirectly
•theexuviae,skin,deadinsects
•theirexcretawhichmakefoodunfitforhumanconsumption
About600speciesofinsectshavebeenassociatedwithstoredgrainproducts
100speciesofinsectpestsofstoredproductscauseeconomiclosses
Compexometric titration/Chelatorphy titration/chelating titration
Classification
Metal ion ion indicators
Masking and demasking reagents
Estimation of Magnisium sulphate
Calcium gluconate
Complexometric Titration/ chelatometry titration/chelating titration, introduction, Types-
1.Direct Titration
2.Back Titration
3.Replacement Titration
4.Indirect Titration
Masking agent, Demasking agents
formation of complex
comparition between masking and demasking agents,
Indicators/Metal ion indicators/ Metallochromic indicators/pM indicators,
Visual Technique,PM indicators (metallochromic), Indicators of pH, Redox Indicators
Instrumental Techniques-Photometry
Potentiometry
Miscellaneous methods.
Complex titration with EDTA.
Bob Reedy - Nitrate in Texas Groundwater.pdf
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The cost of acquiring information by natural selection
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
Equivariant neural networks and representation theory
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
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Speaker: Diego Blas (IFAE/ICREA)
Title: Gravitational wave detection with orbital motion of Moon and artificial
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When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
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Deep analysis of chemical patents and Markush claims
- 1. Beyond the search: Deep analysis of chemical patents and Markush claims Tim Parrott, Árpád Figyelmesi ACS 2018 Boston
- 2. Introduction
- 3. Drug discovery & Patents Early-stage idea validation • Are my structures new compared to the prior art? Patent drafting • Does my claim cover my structure series? • Does my claim cover any structures from the prior art? • Does the application that I already filed cover my new structure series or do I need to file a new one?
- 4. Importance of Markush structures Between 1994 and 2013 3,704,996 US patents 468,262 with Markush claims Every eighth patent contains Markush claims Usually 1010-10100 structures covered by a Markush claim Joseph J. Mallon, 2014
- 5. Markush databases and services • Limited availability and ”black box” nature • Designed for document search for IP experts • Search hits need human interpretation • Not accurate enough for medicinal chemists • Hard/Impossible to integrate with inhouse systems • Closed databases and search engines
- 7. • R-groups • Atom lists • Bond lists • Position variations • Repeating units • Homology groups Markush representation
- 8. Markush technology • Search • Enumeration • Hit visualization • Non-hit visualization • Markush Editor End-User Applications, KNIME, Pipeline Pilot, Java, .Net, REST API
- 9. Computer-assisted Markush extraction • Document annotation • Name to structure • Markush representation • Search and visualization
- 10. Markush extraction view Markush editor Example structures Annotated document Selected structures Structure checker
- 11. • Structural representation of Markush claims • High-quality Markush structures extracted from project- related patents or patent drafts • Analysis of the claimed chemical space Patent curation
- 12. Example workflow Select Documents Extract Markush Claims Validate Structure Ideas Load to Database MRV files Select project-specific patents and curate or send them for curation Use the MRV representation of the Markush claims for automatic, in-house idea validation
- 13. Case Study
- 16. Example structures • 11 structures in the claims • 67 structures in the description
- 17. Are these structures covered?
- 18. Automatic validation 6 structures are not covered!
- 19. Hit and non-hit visualization
- 20. Where is the mistake?
- 21. Process • Markush curation ChemCurator 30 min • Exaples extractions ChemCurator + CLiDE 10 min • Analysis ChemCurator 5 min
- 23. Markush generation Generate Markush structure from from a series of compounds
- 24. Markush to text Generate claim text from the Markush structure
- 25. Conclusion Tools and processes exist to step forward • Write better-quality patents • Understand your competitors’ patents • Integrate IP knowledge into your early- stage discovery process
- 26. THANK YOU tparrott @chemaxon.com ACS 2018 Boston