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Ligand‐based Activity Cliff Prediction Models with Applicability Domain
Author(s) -
Tamura Shunsuke,
Miyao Tomoyuki,
Funatsu Kimito
Publication year - 2020
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.202000103
Subject(s) - extrapolation , applicability domain , predictability , computer science , cliff , matrix metalloproteinase , representation (politics) , cheminformatics , training set , quantitative structure–activity relationship , artificial intelligence , biological system , data mining , machine learning , computational biology , chemistry , mathematics , computational chemistry , biology , mathematical analysis , paleontology , biochemistry , political science , law , statistics , politics
Activity cliffs (ACs) are formed by pairs of structurally similar compounds with large differences in potency. Predicting ACs is of high interest in lead optimization for drug discovery. Previous AC prediction models that focused on matched molecular pair (MMP) cliffs produced adequate performances. However, the extrapolation ability of these models is unclear because the main scaffold for MMPs, the core structure, could exist in both training and test data sets. Also, representation of MMPs did not consider the attachment points where the core and R‐group substituents are connected. In this study, we aimed to improve a ligand‐based AC prediction method using molecular fingerprints. We incorporated applicability domain, which was defined using R‐path fingerprints to consider the local environment around an attachment point. Rigorous evaluation of the extrapolation ability of AC prediction models showed that MMP‐cliffs were accurately predicted for nine biological targets. Furthermore, incorporation of training MMPs with cores distinct from those of test MMPs improved the predictability compared with using training MMPs with only similar cores.