title: Development and implementation of (Q)SAR modeling within the CHARMMing web-user interface.
abstract: 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.
authors.affiliation: Computational Drug Design Systems (CODDES) LLC, Rockville, Maryland, 20852; Laboratory of Computational Biology, NIH, National Heart, Lung, and Blood Institute, Rockville, Maryland, 20852.
authors.forename: Iwona E
authors.initials: IE
authors.lastname: Weidlich
authors.forename: Yuri
authors.initials: Y
authors.lastname: Pevzner
authors.forename: Benjamin T
authors.initials: BT
authors.lastname: Miller
authors.forename: Igor V
authors.initials: IV
authors.lastname: Filippov
authors.forename: H Lee
authors.initials: HL
authors.lastname: Woodcock
authors.forename: Bernard R
authors.initials: BR
authors.lastname: Brooks
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016208:Databases, Factual; D020407:Internet; D008958:Models, Molecular; D021281:Quantitative Structure-Activity Relationship; D012984:Software; D014584:User-Computer Interface
keywords: CHARMMing; QSAR; SAR; machine learning; random forest