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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)
Author(s) -
Rakers Christin,
Najnin Rifat Ara,
Polash Ahsan Habib,
Takeda Shunichi,
Brown J.B.
Publication year - 2018
Publication title -
chemmedchem
Language(s) - English
Resource type - Reports
SCImago Journal Rank - 0.817
H-Index - 100
eISSN - 1860-7187
pISSN - 1860-7179
DOI - 10.1002/cmdc.201800124
Subject(s) - front cover , cover (algebra) , ligand (biochemistry) , computer science , nuclear receptor , computational biology , chemistry , biology , receptor , biochemistry , mechanical engineering , transcription factor , engineering , gene
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. on page 511 in Issue 6, 2018 (DOI: 10.1002/cmdc.201700677).