MM-DRPNet: A multimodal dynamic radial partitioning network for enhanced protein–ligand binding affinity prediction

2024/12/01 by Dayan Liu, Tao Song, Shu-Dong Wang · 1 voice · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · #Bioinformatics and Genomic Networks #Computational Drug Discovery Methods #Protein Structure and Dynamics

paper · doi:10.1016/j.csbj.2024.11.050

openalex publication_date 2024/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/23

Abstract

Accurate prediction of drug-target binding affinity remains a fundamental challenge in contemporary drug discovery. Despite significant advances in computational methods for protein-ligand binding affinity prediction, current approaches still face substantial limitations in prediction accuracy. Moreover, the prevalent methodologies often overlook critical three-dimensional (3D) structural information, thereby constraining their practical utility in computer-aided drug design (CADD). Here we present MM-DRPNet, a multimodal deep learning framework that enhances binding affinity prediction by integrating protein-ligand structural information with interaction features and physicochemical properties. The core innovation lies in our dynamic radial partitioning (DRP) algorithm, which adaptively segments 3D space based on complex-specific interaction patterns, surpassing traditional fixed partitioning methods in capturing spatial interactions. MM-DRPNet further incorporates molecular topological features to comprehensively model both structural and spatial relationships. Extensive evaluations on benchmark datasets demonstrate that MM-DRPNet significantly outperforms state-of-the-art methods across multiple metrics, with ablation studies confirming the substantial contribution of each architectural component. Source code for MM-DRPNet is freely available for download at https://github.com/Bigrock-dd/MMDRPv1.

Citations

• AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading
• Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
• ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing
• Long Short-Term Memory

Cited by

• Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Network with Group Lasso Regularization

Discussions

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