Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information
Iurii Sushko,Sergii Novotarskyi,Robert Körner,Anil Kumar Pandey,Matthias Rupp,Wolfram Teetz,Stefan Brandmaier,Ahmed Abdelaziz,Volodymyr V. Prokopenko,Vsevolod Yu. Tanchuk,Roberto Todeschini,Alexandre Varnek,Gilles Marcou,Peter Ertl,Vladimir Potemkin,Maria Grishina,Johann Gasteiger,Christof H. Schwab,Igor I. Baskin,Vladimir A. Palyulin,Eugene V. Radchenko,William J. Welsh,Vladyslav Kholodovych,Dmitriy Chekmarev,Artem Cherkasov,João Aires-de-Sousa,Qingyou Zhang,Andreas Bender,Florian Nigsch,Luc Patiny,Antony J. Williams,Valery Tkachenko,Igor V. Tetko +32 more
TLDR
The Online Chemical Modeling Environment is a web-based platform that aims to automate and simplify the typical steps required for QSAR modeling and to invite the original authors to contribute their results, make them publicly available, share them with other users and to become members of the growing research community.Abstract:
The Online Chemical Modeling Environment is a web-based platform that aims to automate and simplify the typical steps required for QSAR modeling. The platform consists of two major subsystems: the database of experimental measurements and the modeling framework. A user-contributed database contains a set of tools for easy input, search and modification of thousands of records. The OCHEM database is based on the wiki principle and focuses primarily on the quality and verifiability of the data. The database is tightly integrated with the modeling framework, which supports all the steps required to create a predictive model: data search, calculation and selection of a vast variety of molecular descriptors, application of machine learning methods, validation, analysis of the model and assessment of the applicability domain. As compared to other similar systems, OCHEM is not intended to re-implement the existing tools or models but rather to invite the original authors to contribute their results, make them publicly available, share them with other users and to become members of the growing research community. Our intention is to make OCHEM a widely used platform to perform the QSPR/QSAR studies online and share it with other users on the Web. The ultimate goal of OCHEM is collecting all possible chemoinformatics tools within one simple, reliable and user-friendly resource. The OCHEM is free for web users and it is available online at http://www.ochem.eu.read more
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admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties.
TL;DR: An ADMET structure-activity relationship database that collects, curates, and manages available ADMET-associated properties data from the published literature, and provides a user-friendly interface to query a specific chemical profile, using either CAS registry number, common name, or structure similarity.
Journal ArticleDOI
ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties
Guo-Li Xiong,Zhenxing Wu,Jiacai Yi,Li Fu,Zhi-Jiang Yang,Chang-Yu Hsieh,Ming-Zhu Yin,Xiangxiang Zeng,Chengkun Wu,Aiping Lu,Chen Xiang,Tingjun Hou,Dong-Sheng Cao,Dong-Sheng Cao +13 more
TL;DR: ADMETlab 2.0 as discussed by the authors is a completely redesigned version of the widely used AMDETlab web server for the predictions of pharmacokinetics and toxicity properties of chemicals, of which the supported ADMET-related endpoints are approximately twice the number of the endpoints in the previous version, including 17 physicochemical properties, 13 medicinal chemistry properties, 23 ADME properties, 27 toxicity endpoints and 8 toxicophore rules.
Journal ArticleDOI
Recognizing Pitfalls in Virtual Screening: A Critical Review
Thomas Scior,Andreas Bender,Gary Tresadern,José L. Medina-Franco,Karina Martinez-Mayorga,Thierry Langer,Karina Cuanalo-Contreras,Dimitris K. Agrafiotis +7 more
TL;DR: This review attempts to catalogue published and unpublished problems, shortcomings, failures, and technical traps of VS methods with the aim to avoid pitfalls by making the user aware of them in the first place.
Journal ArticleDOI
CERAPP: Collaborative Estrogen Receptor Activity Prediction Project
Kamel Mansouri,Ahmed Abdelaziz,Aleksandra Rybacka,Alessandra Roncaglioni,Alexander Tropsha,Alexandre Varnek,Alexey V. Zakharov,Andrew Worth,Ann M. Richard,Christopher M. Grulke,Daniela Trisciuzzi,Denis Fourches,Dragos Horvath,Emilio Benfenati,Eugene N. Muratov,Eva Bay Wedebye,Francesca Grisoni,Giuseppe Felice Mangiatordi,Giuseppina M. Incisivo,Huixiao Hong,Hui W. Ng,Igor V. Tetko,Ilya A. Balabin,Jayaram Kancherla,Jie Shen,Julien Burton,Marc C. Nicklaus,Matteo Cassotti,Nikolai Georgiev Nikolov,Orazio Nicolotti,Patrik L. Andersson,Qingda Zang,Regina Politi,Richard D. Beger,Roberto Todeschini,Ruili Huang,Sherif Farag,Sine Abildgaard Rosenberg,Svetoslav Slavov,Xin Hu,Richard S. Judson +40 more
TL;DR: This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches and the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing.
Journal ArticleDOI
Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.
TL;DR: The aim was to assess whether deep learning offered any improvement in testing when assessed using an array of metrics including AUC, F1 score, Cohen's kappa, Matthews correlation coefficient and others, and based on ranked normalized scores for the metrics or data sets Deep Neural Networks (DNN) ranked higher than SVM, which in turn wasranked higher than all the other machine learning methods.
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