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

Iwona E. Weidlich, Yuri Pevzner, Benjamin T. Miller, Igor V. Filippov, H. Lee Woodcock, Bernard R. Brooks
2014 Journal of Computational Chemistry  
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 SAR and QSAR modeling to add to the services provided by CHARMMing ( This new module implements some of the most recent advances in modern machine learning algorithms -Random Forest, Support Vector Machine (SVM), Stochastic Gradient Descent, Gradient Tree Boosting etc. A user can import training data
more » ... 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.
doi:10.1002/jcc.23765 pmid:25362883 pmcid:PMC4244250 fatcat:lfh4es7be5hf5byb4nqk6jg7ja