A Simple Representation of Three-Dimensional Molecular Structure

作者：Axen Seth D; Huang Xi Ping; Caceres Elena L; Gendelev Leo; Roth Bryan L; Keiser Michael J

来源：Journal of Medicinal Chemistry, 2017, 60(17): 7393-7409.

DOI：10.1021/acs.jmedchem.7b00696

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.

• 出版日期2017-9-14