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bioRxiv · 10.1101/136705

A Simple Representation Of Three-Dimensional Molecular Structure

Axen, S. D.·Huang, X.-P.·Caceres, E. L.·Gendelev, L.·Roth, B. L.·Keiser, M.
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Abstract

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.

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Axen, S. D., Huang, X.-P., Caceres, E. L., Gendelev, L., Roth, B. L., Keiser, M.. 2017-05-11. A Simple Representation Of Three-Dimensional Molecular Structure. https://doi.org/10.1101/136705

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