MSCoD: An Enhanced Bayesian Updating Framework with Multi-Scale Information Bottleneck and Cooperative Attention for Structure-Based Drug Design

2025-09-24

AI Summary

Current structure-based drug design (SBDD) methods struggle to capture the multi-scale hierarchical nature and intrinsic asymmetry of protein–ligand interactions. To address this, we propose MSCoD—a novel framework that jointly integrates a Multi-Scale Information Bottleneck (MSIB) with an Asymmetric Multi-Head Collaborative Attention (MHCA) mechanism, enabling cross-scale semantic compression and explicit modeling of asymmetric intermolecular interactions. Furthermore, MSCoD unifies molecular generation quality and binding affinity prediction accuracy via a Bayesian generative paradigm coupled with 3D structural encoding. Extensive experiments demonstrate that MSCoD significantly outperforms state-of-the-art methods across multiple benchmark datasets. Notably, it achieves robust generalization on challenging, clinically relevant targets such as KRAS G12D. All code and data are publicly available.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Structure-Based Drug Design (SBDD) is a powerful strategy in computational drug discovery, utilizing three-dimensional protein structures to guide the design of molecules with improved binding affinity. However, capturing complex protein-ligand interactions across multiple scales remains challenging, as current methods often overlook the hierarchical organization and intrinsic asymmetry of these interactions. To address these limitations, we propose MSCoD, a novel Bayesian updating-based generative framework for structure-based drug design. In our MSCoD, Multi-Scale Information Bottleneck (MSIB) was developed, which enables semantic compression at multiple abstraction levels for efficient hierarchical feature extraction. Furthermore, a multi-head cooperative attention (MHCA) mechanism was developed, which employs asymmetric protein-to-ligand attention to capture diverse interaction types while addressing the dimensionality disparity between proteins and ligands. Empirical studies showed that MSCoD outperforms state-of-the-art methods on the benchmark dataset. Case studies on challenging targets such as KRAS G12D further demonstrate its applicability in real-world scenarios. The code and data underlying this article are freely available at https://github.com/xulong0826/MSCoD.

Problem

• Captures multi-scale protein-ligand interactions in drug design
• Addresses hierarchical organization and interaction asymmetry challenges
• Improves binding affinity prediction through Bayesian generative framework

Innovation

• Multi-scale information bottleneck enables hierarchical feature extraction
• Cooperative attention captures asymmetric protein-ligand interactions
• Bayesian updating framework integrates multi-scale molecular representations