Front Cover: Chemogenomic Active Learning's Domain of Applicability on Small, Sparse qHTS Matrices: A Study Using Cytochrome P450 and Nuclear Hormone Receptor Families (ChemMedChem 6/2018). (25th March 2018)

• Record Type: Journal Article
• Authors: Rakers, Christin; Najnin, Rifat Ara; Polash, Ahsan Habib; Takeda, Shunichi; Brown, J.B.
• Is Part Of: ChemMedChem. Volume 13:Number 6(2018)
• Page Start: 464
• Page End: 464
• Publication Date: 2018-03-25
• Subjects: active learning -- chemogenomics -- chemoinformatics -- cytochromes -- hormone receptors; Pharmaceutical chemistry -- Periodicals; 615.19005
• DOI: 10.1002/cmdc.201800124
• Languages: English
• ISSNs: 1860-7179

Abstract

Abstract : The Front Cover shows an emerging method for data mining ligand–target bioactivity matrices known as Chemogenomic Active Learning. As ligand–target pairs labeled as actives and inactives are systematically picked one‐by‐one, a set of decision trees serving as rules to explain active and inactive bioactivity is built. In this article, Rakers et al. show how the method can effectively model nuclear hormone receptor (NHR) and cytochrome P450 (CYP450) family‐wide ligand–target interaction with only a fraction of available data. An actively learned model can be updated after new ligands are synthesized and assayed for activity, converging on hit discovery and optimization more effectively than brute‐force screening. Cover artwork by Christin Rakers and J.B. Brown. More information can be found in the Full Paper by J.B. Brown et al. onpage 511 in Issue 6, 2018 (DOI:10.1002/cmdc.201700677 ).