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Large‐Scale QSAR in Target Prediction and Phenotypic HTS Assessment
Author(s) -
Jenkins Jeremy L.
Publication year - 2012
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201200002
Subject(s) - quantitative structure–activity relationship , in silico , computer science , standardization , computational biology , probabilistic logic , scale (ratio) , machine learning , artificial intelligence , biology , genetics , physics , quantum mechanics , gene , operating system
The advent of in silico compound target prediction offers a potential paradigm shift in how large compound collections are understood and used strategically in high‐throughput screens (HTS). Specifically, phenotypic HTS hits may be annotated both with known targets and predicted targets using large‐scale QSAR models, enabling a more sophisticated hit assessment. Efforts in massive bioactivity data integration and standardization is empowering such compound‐target annotations. These approaches differ fundamentally from the traditional role of QSAR in lead optimization and binding affinity predictions to global, probabilistic target predictions for thousands of human proteins.