Journal
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Volume 19, Issue 1, Pages 407-417Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2020.3046945
Keywords
Binding affinity prediction; deep learning; efficient 3D-CNN; benchmarking
Categories
Funding
- National Science Foundation [CNS-1842407]
- National Institutes of Health [R01GM110240, R01NS088437, R01CA212403]
Ask authors/readers for more resources
This paper introduces a data-driven framework called DeepAtom for accurately predicting protein-ligand binding affinity. By utilizing a 3D Convolutional Neural Network (3D-CNN) architecture, DeepAtom can automatically extract atomic interaction patterns related to binding. Experiment results demonstrate that the DeepAtom approach outperforms other methods in baseline scoring and can potentially be adopted in computational drug development protocols.
Computational drug design relies on the calculation of binding strength between two biological counterparts especially a chemical compound, i.e., a ligand, and a protein. Predicting the affinity of protein-ligand binding with reasonable accuracy is crucial for drug discovery, and enables the optimization of compounds to achieve better interaction with their target protein. In this paper, we propose a data-driven framework named DeepAtom to accurately predict the protein-ligand binding affinity. With 3D Convolutional Neural Network (3D-CNN) architecture, DeepAtom could automatically extract binding related atomic interaction patterns from the voxelized complex structure. Compared with the other CNN based approaches, our light-weight model design effectively improves the model representational capacity, even with the limited available training data. We carried out validation experiments on the PDBbind v.2016 benchmark and the independent Astex Diverse Set. We demonstrate that the less feature engineering dependent DeepAtom approach consistently outperforms the other baseline scoring methods. We also compile and propose a new benchmark dataset to further improve the model performances. With the new dataset as training input, DeepAtom achieves Pearson's R=0.83 and RMSE=1.23 pK units on the PDBbind v.2016 core set. The promising results demonstrate that DeepAtom models can be potentially adopted in computational drug development protocols such as molecular docking and virtual screening.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available