A point cloud-based deep learning strategy for protein–ligand binding affinity prediction. Issue 1 (25th November 2021)

• Record Type: Journal Article
• Authors: Wang, Yeji; Wu, Shuo; Duan, Yanwen; Huang, Yong
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 1(2022)
• Publication Date: 2021-11-25
• Subjects: point cloud -- deep learning -- ligands -- PointNet -- PointTransformer; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbab474
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: There is great interest to develop artificial intelligence-based protein–ligand binding affinity models due to their immense applications in drug discovery. In this paper, PointNet and PointTransformer, two pointwise multi-layer perceptrons have been applied for protein–ligand binding affinity prediction for the first time. Three-dimensional point clouds could be rapidly generated from PDBbind-2016 with 3772 and 11 327 individual point clouds derived from the refined or/and general sets, respectively. These point clouds (the refined or the extended set) were used to train PointNet or PointTransformer, resulting in protein–ligand binding affinity prediction models with Pearson correlation coefficients R = 0.795 or 0.833 from the extended data set, respectively, based on the CASF-2016 benchmark test. The analysis of parameters suggests that the two deep learning models were capable to learn many interactions between proteins and their ligands, and some key atoms for the interactions could be visualized. The protein–ligand interaction features learned by PointTransformer could be further adapted for the XGBoost-based machine learning algorithm, resulting in prediction models with an average Rp of 0.827, which is on par with state-of-the-art machine learning models. These results suggest that the point clouds derived from PDBbind data sets are useful to evaluate the performance of 3D point clouds-centered deep learning algorithms, which could learn atomic features of protein–ligand interactions from natural evolution or medicinal chemistry and thus have wide applications in chemistry and biology.