A Simple Representation of Three-Dimensional Molecular Structure

Statistical
and machine learning approaches predict drug-to-target
relationships from 2D small-molecule topology patterns. One might
expect 3D information to improve these calculations. Here we apply
the logic of the extended connectivity fingerprint (ECFP) to develop
a rapid, alignment-invariant 3D representation of molecular conformers,
the extended three-dimensional fingerprint (E3FP). By integrating
E3FP with the similarity ensemble approach (SEA), we achieve higher
precision-recall performance relative to SEA with ECFP on ChEMBL20
and equivalent receiver operating characteristic performance. We identify
classes of molecules for which E3FP is a better predictor of similarity
in bioactivity than is ECFP. Finally, we report novel drug-to-target
binding predictions inaccessible by 2D fingerprints and confirm three
of them experimentally with ligand efficiencies from 0.442–0.637
kcal/mol/heavy atom.