Deep Learning in Drug Design : Protein-Ligand Binding Affinity Prediction

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

24 Scopus Citations
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  • Mohammad A. Rezaei
  • Yanjun Li
  • Dapeng Wu
  • Xiaolin Li
  • Chenglong Li


Original languageEnglish
Pages (from-to)407-417
Journal / PublicationIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number1
Online published23 Dec 2020
Publication statusPublished - Jan 2022
Externally publishedYes


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.

Research Area(s)

  • benchmarking, Binding affinity prediction, deep learning, efficient 3D-CNN