J Chem Inf Model - Improving classical substructure-based virtual screening to handle extrapolation challenges.

Tópicos

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Resumo

Target-oriented substructure-based virtual screening (sSBVS) of molecules is a promising approach in drug discovery. Yet, there are doubts whether sSBVS is suitable also for extrapolation, that is, for detecting molecules that are very different from those used for training. Herein, we evaluate the predictive power of classic virtual screening methods, namely, similarity searching using Tanimoto coefficient (MTC) and Naive Bayes (NB). As could be expected, these classic methods perform better in interpolation than in extrapolation tasks. Consequently, to enhance the predictive ability for extrapolation tasks, we introduce the Shadow approach, in which inclusion relations between substructures are considered, as opposed to the classic sSBVS methods that assume independence between substructures. Specifically, we discard contributions from substructures included in ("shaded" by) others which are, in turn, included in the molecule of interest. Indeed, the Shadow classifier significantly outperforms both MTC (pValue = 3.1 ? 10(-16)) and NB (pValue = 3.5 ? 10(-9)) in detecting hits sharing low similarity with the training active molecules.

Resumo Limpo

targetori substructurebas virtual screen ssbvs molecul promis approach drug discoveri yet doubt whether ssbvs suitabl also extrapol detect molecul differ use train herein evalu predict power classic virtual screen method name similar search use tanimoto coeffici mtc naiv bay nb expect classic method perform better interpol extrapol task consequ enhanc predict abil extrapol task introduc shadow approach inclus relat substructur consid oppos classic ssbvs method assum independ substructur specif discard contribut substructur includ shade other turn includ molecul interest inde shadow classifi signific outperform mtc pvalu nb pvalu detect hit share low similar train activ molecul

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