Development and Implementation of (Q)SAR Modeling Within the CHARMMing Web-User Interface

作者：Weidlich Iwona E^*; Pevzner Yuri; Miller Benjamin T; Filippov Igor V; Woodcock H Lee; Brooks Bernard R

来源：Journal of Computational Chemistry, 2015, 36(1): 62-67.

DOI：10.1002/jcc.23765

• CHARMMing
• SAR
• QSAR
• machine learning
• random forest

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 (). This new module implements some of the most recent advances in modern machine learning algorithmsRandom 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.

• 出版日期2015-1-5
• 单位NIH