BioScore：面向多样生物分子复合物的基础打分函数
BioScore: A Foundational Scoring Function For Diverse Biomolecular Complexes
AI总结：
针对现有打分函数在多样生物分子系统间缺乏泛化能力的问题，提出基础打分函数BioScore，通过双尺度几何图学习框架支持多种任务，在16个基准上匹敌70种方法，实现显著的零样本预测提升并有效处理环肽等复杂系统。
摘要：
生物分子复合物的结构评估对于将分子模型转化为功能洞察至关重要，它塑造了我们对生物学的理解并辅助药物发现。然而，当前基于结构的打分函数往往缺乏在多样生物分子系统间的泛化能力。我们提出了BioScore，这是一种基础打分函数，通过一个双尺度几何图学习框架以及为结构评估和亲和力预测定制的模块，解决了数据稀疏、跨系统表示和任务兼容性等关键挑战。BioScore支持广泛的任务，包括亲和力预测、构象排序和基于结构的虚拟筛选。在涵盖蛋白质、核酸、小分子和碳水化合物的16个基准上进行评估，BioScore始终优于或匹敌70种传统和深度学习方法。我们新提出的PPI Benchmark进一步实现了对蛋白质-蛋白质复合物打分的全面评估。BioScore展示了广泛的适用性：(1) 在混合结构数据上进行预训练，使蛋白质-蛋白质亲和力预测提升高达40%，抗原-抗体结合相关性提升超过90%；(2) 跨系统泛化能力实现了零样本和少样本预测，相关性增益高达71%；(3) 其统一表示能够捕捉如环肽等具有化学挑战性的系统，将亲和力预测提升超过60%。BioScore为跨越复杂生物分子景观的结构评估建立了一个稳健且可泛化的框架。
英文摘要：
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.
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