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J Chem Inf Model. 2016 Sep 26;56(9):1631-40. doi: 10.1021/acs.jcim.6b00359. Epub 2016 Aug 26.

Prediction of Activity Cliffs Using Condensed Graphs of Reaction Representations, Descriptor Recombination, Support Vector Machine Classification, and Support Vector Regression.

Author information

1
Laboratoire de Chemoinformatique, UMR 7140, Université de Strasbourg , 1 rue Blaise Pascal, Strasbourg 67000, France.
2
Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität , Dahlmannstrasse 2, D-53113 Bonn, Germany.

Abstract

Activity cliffs (ACs) are formed by structurally similar compounds with large differences in activity. Accordingly, ACs are of high interest for the exploration of structure-activity relationships (SARs). ACs reveal small chemical modifications that result in profound biological effects. The ability to foresee such small chemical changes with significant biological consequences would represent a major advance for drug design. Nevertheless, only few attempts have been made so far to predict whether a pair of analogues is likely to represent an AC-and even fewer went further to quantitatively predict how "deep" a cliff might be. This might be due to the fact that such predictions must focus on compound pairs. Matched molecular pairs (MMPs), defined as pairs of structural analogs that are only distinguished by a chemical modification at a single site, are a preferred representation of ACs. Herein, we report new strategies for AC prediction that are based upon two different approaches: (i) condensed graphs of reactions, which were originally introduced for modeling of chemical reactions and were here adapted to encode MMPs, and, (ii) plain descriptor recombination-a strategy used for quantitative structure-property relationship (QSPR) modeling of nonadditive mixtures (MQSPR). By applying these concepts, ACs were encoded as single descriptor vectors used as input for support vector machine (SVM) classification and support vector regression (SVR), yielding accurate predictions of AC status (i.e., cliff vs noncliff) and potency differences, respectively. The latter were predicted in a compound order-sensitive manner returning the signed value of expected potency differences between AC compounds.

PMID:
27564682
DOI:
10.1021/acs.jcim.6b00359
[Indexed for MEDLINE]

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