Abstract：
: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.
journal_name
J Chem Inf Modeljournal_title
Journal of chemical information and modelingauthors
Biniashvili T,Schreiber E,Kliger Ydoi
10.1021/ci200472ssubject
Has Abstractpub_date
2012-03-26 00:00:00pages
678-85issue
3eissn
1549-9596issn
1549-960Xjournal_volume
52pub_type
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