Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information

By Iurii Sushko, S. Novotarskyi, R. Körner, Anil Kumar Pandey, Matthias Rupp , Wolfram Teetz, S. Brandmaier, A. Abdelaziz, V. Prokopenko, V. Tanchuk and 23 more. J. Comput. Aided Mol. Des., 2011

610 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6c0bd01f83c48005122727d6285d4e88b3652175

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Citations in the corpus

The development of models to predict melting and pyrolysis point data associated with several hundred thousand compounds mined from PATENTS (2016, methodology): “The final consensus model was compared (Table 6) with the model developed using the COMBINED set in our previous publication [29].”
ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support (2024, positive): “We incorporated a wide spectrum of open-access bioactivity databases including ChEMBL ( 16 ), PubChem ( 17 ) and OCHEM ( 18 ), as well as rigorously peer-reviewed literature ( 19–24 ).”
Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets. (2017, background): “…to develop open platforms for connecting scientists and sharing data for many types of projects relevant to drug discovery, these will often include various prediction algorithms e.g. qsardb.org and ochem.eu 99, 100 or private sharing and development tools such as Chembench 101 and CDD Vault 62 .”