Prediction of Compound Potency Changes in Matched Molecular Pairs Using Support Vector Regression

Matched molecular pairs (MMPs) consist of pairs of compounds that are transformed into each other by a substructure exchange. If MMPs are formed by compounds sharing the same biological activity, they encode a potency change. If the potency difference between MMP compounds is very small, the substructure exchange (chemical transformation) encodes a bioisosteric replacement; if the difference is very large, the transformation encodes an activity cliff. For a given compound activity class, MMPs comprehensively capture existing structural relationships and represent a spectrum of potency changes for structurally analogous compounds. We have aimed to predict potency changes encoded by MMPs. This prediction task principally differs from conventional quantitative structure-activity relationship (QSAR) analysis. For the prediction of MMP-associated potency changes, we introduce direction-dependent MMPs and combine MMP analysis with support vector regression (SVR) modeling. Combinations of newly designed kernel functions and fingerprint descriptors are explored. The resulting SVR models yield accurate predictions of MMP-encoded potency changes for many different data sets. Shared key structure context is found to contribute critically to prediction accuracy. SVR models reach higher performance than random forest (RF) and MMP-based averaging calculations carried out as controls. A comparison of SVR with kernel ridge regression indicates that prediction accuracy has largely been a consequence of kernel characteristics rather than SVR optimization details.