3D Chemical Similarity Networks for Structure-Based Target Prediction and Scaffold Hopping

Target identification remains a major
challenge for modern drug discovery programs aimed at understanding
the molecular mechanisms of drugs. Computational target prediction
approaches like 2D chemical similarity searches have been widely used
but are limited to structures sharing high chemical similarity. Here,
we present a new computational approach called chemical similarity
network analysis pull-down 3D (CSNAP3D) that combines 3D chemical
similarity metrics and network algorithms for structure-based drug
target profiling, ligand deorphanization, and automated identification
of scaffold hopping compounds. In conjunction with 2D chemical similarity
fingerprints, CSNAP3D achieved a >95% success rate in correctly
predicting the drug targets of 206 known drugs. Significant improvement
in target prediction was observed for HIV reverse transcriptase (HIVRT)
compounds, which consist of diverse scaffold hopping compounds targeting
the nucleotidyltransferase binding site. CSNAP3D was further applied
to a set of antimitotic compounds identified in a cell-based chemical
screen and identified novel small molecules that share a pharmacophore
with Taxol and display a Taxol-like mechanism of action, which were
validated experimentally using in vitro microtubule
polymerization assays and cell-based assays.