title: QSAR modeling of imbalanced high-throughput screening data in PubChem.
abstract: Many of the structures in PubChem are annotated with activities determined in high-throughput screening (HTS) assays. Because of the nature of these assays, the activity data are typically strongly imbalanced, with a small number of active compounds contrasting with a very large number of inactive compounds. We have used several such imbalanced PubChem HTS assays to test and develop strategies to efficiently build robust QSAR models from imbalanced data sets. Different descriptor types [Quantitative Neighborhoods of Atoms (QNA) and "biological" descriptors] were used to generate a variety of QSAR models in the program GUSAR. The models obtained were compared using external test and validation sets. We also report on our efforts to incorporate the most predictive of our models in the publicly available NCI/CADD Group Web services ( http://cactus.nci.nih.gov/chemical/apps/cap).
authors: CADD Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health , DHHS, NCI-Frederick, 376 Boyles St., Frederick, Maryland 21702, United States.
authors: Alexey V
authors: AV
authors: Zakharov
authors: Megan L
authors: ML
authors: Peach
authors: Markus
authors: M
authors: Sitzmann
authors: Marc C
authors: MC
authors: Nicklaus
mesh_terms: D000465:Algorithms; D062126:Databases, Chemical; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D057809:HEK293 Cells; D057166:High-Throughput Screening Assays / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D021281:Quantitative Structure-Activity Relationship; D054852:Small Molecule Libraries / Q000737:chemistry* / Q000494:pharmacology*; D012984:Software
chemical_list: D054852:Small Molecule Libraries