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Protein–Ligand Scoring with Convolutional Neural Networks
Abstract
Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. Structure-based drug design methods rely on scoring functions to rank and predict binding affinities and poses. The ever-expanding amount of protein–ligand binding and structural data enables the use of deep machine learning techniques for protein–ligand scoring. We describe convolutional neural network (CNN) scoring functions that take as input a comprehensive three-dimensional (3D) representation of a protein–ligand interaction. A CNN scoring function automatically learns the key features of protein–ligand interactions that correlate with binding. We train and optimize our CNN scoring functions to discriminate between correct and incorrect binding poses and known binders and nonbinders. We find that our CNN scoring function outperforms the AutoDock Vina scoring function when ranking poses both for pose prediction and virtual screening- Text
- Journal contribution
- Biophysics
- Biochemistry
- Neuroscience
- Pharmacology
- Biotechnology
- Mental Health
- Infectious Diseases
- Computational Biology
- Biological Sciences not elsewhere classified
- Information Systems not elsewhere classified
- binding affinities
- protein
- AutoDock Vina
- Convolutional Neural Networks Computational approaches
- drug discovery
- function
- CNN
- novel chemotypes
- Structure-based drug design methods