Development and implementation of (Q)SAR modeling within the CHARMMing web-user interface

• Published in: Journal of computational chemistry Vol. 36; no. 1; pp. 62 - 67
• Main Authors: Weidlich, Iwona E., Pevzner, Yuri, Miller, Benjamin T., Filippov, Igor V., Woodcock, H. Lee, Brooks, Bernard R.
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 05.01.2015; Wiley Subscription Services, Inc
• Subjects: Algorithms; Artificial Intelligence; Bioassays; Biochemistry; CHARMMing; Chemical compounds; Databases, Factual; Internet; machine learning; Models, Molecular; QSAR; Quantitative Structure-Activity Relationship; random forest; SAR; Software; User interface; User-Computer Interface; random forest; machine learning; QSAR; SAR; CHARMMing
• ISSN: 0192-8651; 1096-987X
• DOI: 10.1002/jcc.23765

Recent availability of large publicly accessible databases of chemical compounds and their biological activities (PubChem, ChEMBL) has inspired us to develop a web‐based tool for structure activity relationship and quantitative structure activity relationship modeling to add to the services provided by CHARMMing (www.charmming.org). This new module implements some of the most recent advances in modern machine learning algorithms—Random Forest, Support Vector Machine, Stochastic Gradient Descent, Gradient Tree Boosting, so forth. A user can import training data from Pubchem Bioassay data collections directly from our interface or upload his or her own SD files which contain structures and activity information to create new models (either categorical or numerical). A user can then track the model generation process and run models on new data to predict activity. © 2014 Wiley Periodicals, Inc. An easy‐to‐use web tool to mine the PubChem BioAssay database and develop novel structure activity models based on modern machine learning approaches.