Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.10877 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908449757986816 |
|---|---|
| author | Zhu, Yuchen Chen, Jihong Li, Yitong Fang, Xiaomin Ye, Xianbin He, Jingzhou Zhang, Xujun Ge, Jingxuan Shen, Chao Zhang, Xiaonan Hou, Tingjun Hsieh, Chang-Yu |
| author_facet | Zhu, Yuchen Chen, Jihong Li, Yitong Fang, Xiaomin Ye, Xianbin He, Jingzhou Zhang, Xujun Ge, Jingxuan Shen, Chao Zhang, Xiaonan Hou, Tingjun Hsieh, Chang-Yu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_10877 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BioScore: A Foundational Scoring Function For Diverse Biomolecular Complexes Zhu, Yuchen Chen, Jihong Li, Yitong Fang, Xiaomin Ye, Xianbin He, Jingzhou Zhang, Xujun Ge, Jingxuan Shen, Chao Zhang, Xiaonan Hou, Tingjun Hsieh, Chang-Yu Chemical Physics Machine Learning Biological Physics 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. |
| title | BioScore: A Foundational Scoring Function For Diverse Biomolecular Complexes |
| topic | Chemical Physics Machine Learning Biological Physics |
| url | https://arxiv.org/abs/2507.10877 |