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A Simple Representation of Three-Dimensional Molecular Structure.


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.

SUBMITTER: Axen SD 

PROVIDER: S-EPMC6075869 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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A Simple Representation of Three-Dimensional Molecular Structure.

Axen Seth D SD   Huang Xi-Ping XP   Cáceres Elena L EL   Gendelev Leo L   Roth Bryan L BL   Keiser Michael J MJ  

Journal of medicinal chemistry 20170808 17


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 performa  ...[more]

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