J Chem Inf Model - Prediction of compound potency changes in matched molecular pairs using support vector regression.

Tópicos

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Resumo

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.

Resumo Limpo

match molecular pair mmps consist pair compound transform substructur exchang mmps form compound share biolog activ encod potenc chang potenc differ mmp compound small substructur exchang chemic transform encod bioisoster replac differ larg transform encod activ cliff given compound activ class mmps comprehens captur exist structur relationship repres spectrum potenc chang structur analog compound aim predict potenc chang encod mmps predict task princip differ convent quantit structureact relationship qsar analysi predict mmpassoci potenc chang introduc directiondepend mmps combin mmp analysi support vector regress svr model combin newli design kernel function fingerprint descriptor explor result svr model yield accur predict mmpencod potenc chang mani differ data set share key structur context found contribut critic predict accuraci svr model reach higher perform random forest rf mmpbase averag calcul carri control comparison svr kernel ridg regress indic predict accuraci larg consequ kernel characterist rather svr optim detail

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