3 years ago

A Simple Representation of Three-Dimensional Molecular Structure

A Simple Representation of Three-Dimensional Molecular Structure
Leo Gendelev, Xi-Ping Huang, Elena L. Cáceres, Seth D. Axen, Michael J. Keiser, Bryan L. Roth
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.

Publisher URL: http://dx.doi.org/10.1021/acs.jmedchem.7b00696

DOI: 10.1021/acs.jmedchem.7b00696

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