A Simple Representation Of Three-Dimensional Molecular Structure [article]

Seth D. Axen, Xi-Ping Huang, Elena L. Caceres, Leo Gendelev, Bryan L. Roth, Michael J. Keiser
2017 bioRxiv   pre-print
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
more » ... nce 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.
doi:10.1101/136705 fatcat:spjnyzatzvcfri5insngzrysvm