A Simple Representation of Three-Dimensional Molecular Structure.
Seth D AxenXi-Ping HuangElena L CáceresLeo GendelevBryan L RothMichael J KeiserPublished in: Journal of medicinal chemistry (2017)
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.
Keyphrases
- small molecule
- machine learning
- molecular dynamics
- neural network
- density functional theory
- adverse drug
- single molecule
- quality control
- drug induced
- artificial intelligence
- protein protein
- convolutional neural network
- big data
- molecular dynamics simulations
- emergency department
- multiple sclerosis
- health information
- electron transfer