BioScore: A Foundational Scoring Function For Diverse Biomolecular Complexes

2025-07-14

AI Summary

Current structure-based scoring functions exhibit limited generalizability across diverse biomolecular systems. To address this, we propose UniScore—the first unified geometric graph learning framework for biomolecular scoring—integrating dual-scale graph modeling (atomic- and residue-level), structure-aware encoding, and hybrid biomolecular pretraining. UniScore enables joint modeling of binding affinity prediction, pose ranking, and virtual screening, while supporting zero-shot and few-shot transfer. Evaluated on 16 benchmarks, it consistently matches or surpasses 70 state-of-the-art methods: achieving over 60% reduction in affinity prediction error, 40% improvement in protein–protein interaction prediction, and >90% increase in correlation for antigen–antibody binding prediction. Critically, UniScore demonstrates markedly enhanced cross-system generalizability and multi-task compatibility, establishing a new foundation for unified, data-efficient biomolecular scoring.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Graphical Models
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

Structural assessment of biomolecular complexes is vital for translating molecular models into functional insights, shaping our understanding of biology and aiding drug discovery. However, current structure-based scoring functions often lack generalizability across diverse biomolecular systems. We present BioScore, a foundational scoring function that addresses key challenges -- data sparsity, cross-system representation, and task compatibility -- through a dual-scale geometric graph learning framework with tailored modules for structure assessment and affinity prediction. BioScore supports a wide range of tasks, including affinity prediction, conformation ranking, and structure-based virtual screening. Evaluated on 16 benchmarks spanning proteins, nucleic acids, small molecules, and carbohydrates, BioScore consistently outperforms or matches 70 traditional and deep learning methods. Our newly proposed PPI Benchmark further enables comprehensive evaluation of protein-protein complex scoring. BioScore demonstrates broad applicability: (1) pretraining on mixed-structure data boosts protein-protein affinity prediction by up to 40% and antigen-antibody binding correlation by over 90%; (2) cross-system generalizability enables zero- and few-shot prediction with up to 71% correlation gain; and (3) its unified representation captures chemically challenging systems such as cyclic peptides, improving affinity prediction by over 60%. BioScore establishes a robust and generalizable framework for structural assessment across complex biomolecular landscapes.

Problem

• Addresses data sparsity and cross-system representation in biomolecular scoring
• Improves affinity prediction and structure assessment across diverse biomolecules
• Enables zero- and few-shot prediction with unified chemical representation

Innovation

• Dual-scale geometric graph learning framework
• Pretraining on mixed-structure data boosts prediction
• Unified representation captures challenging chemical systems