ABSTRACT: We developed a novel approach called SHAFTS (SHApe-
FeaTure Similarity) for 3D molecular similarity calculation and ligandbased
virtual screening. SHAFTS adopts a hybrid similarity metric
combined with molecular shape and colored (labeled) chemistry groups
annotated by pharmacophore features for 3D similarity calculation and
ranking, which is designed to integrate the strength of pharmacophore
matching and volumetric overlay approaches. A feature triplet hashing
method is used for fast molecular alignment poses enumeration, and the
optimal superposition between the target and the query molecules can
be prioritized by calculating corresponding “hybrid similarities”.
SHAFTS is suitable for large-scale virtual screening with single or
multiple bioactive compounds as the query “templates” regardless of whether corresponding experimentally determined
conformations are available. Two public test sets (DUD and Jain’s sets) including active and decoy molecules from a panel of
useful drug targets were adopted to evaluate the virtual screening performance. SHAFTS outperformed several other widely used
virtual screening methods in terms of enrichment of known active compounds as well as novel chemotypes, thereby indicating its
robustness in hit compounds identification and potential of scaffold hopping in virtual screening.