轻量模型快速预测药物分子结合构象，兼顾速度与可解释性。
QuickBind: A Light-Weight And Interpretable Molecular Docking Model
- 采用轻量化设计，实现高效分子对接构象预测。
- 在多个基准测试中表现良好，速度远超传统方法。
- 模型结构简单可解释，适合药物筛选与新架构探索。
预测配体与靶蛋白的结合构象是早期计算药物发现的关键环节。近年来机器学习方法虽提升了构象精度，但牺牲了运行效率。针对高通量虚拟筛选需求，我们开发了QuickBind——一种轻量化且可解释的构象预测算法。在多个常用基准上评估显示，该模型在准确率与运行时间间取得良好平衡。为支持虚拟筛选，我们进一步集成结合亲和力模块，并在多个临床相关药物靶点上验证其性能。通过机制分析发现，QuickBind已学习到分子对接中的关键物理化学特性，揭示了机器学习模型生成结合构象的内在逻辑。凭借其简洁性，QuickBind既可作为有效的虚拟筛选工具，也可作为新型模型架构探索的最小化实验平台。模型代码与权重已公开于https://github.com/aqlaboratory/QuickBind。
原文摘要 · Abstract (English)
Predicting a ligand's bound pose to a target protein is a key component of early-stage computational drug discovery. Recent developments in machine learning methods have focused on improving pose quality at the cost of model runtime. For high-throughput virtual screening applications, this exposes a capability gap that can be filled by moderately accurate but fast pose prediction. To this end, we developed QuickBind, a light-weight pose prediction algorithm. We assess QuickBind on widely used benchmarks and find that it provides an attractive trade-off between model accuracy and runtime. To facilitate virtual screening applications, we augment QuickBind with a binding affinity module and demonstrate its capabilities for multiple clinically-relevant drug targets. Finally, we investigate the mechanistic basis by which QuickBind makes predictions and find that it has learned key physicochemical properties of molecular docking, providing new insights into how machine learning models generate protein-ligand poses. By virtue of its simplicity, QuickBind can serve as both an effective virtual screening tool and a minimal test bed for exploring new model architectures and innovations. Model code and weights are available at https://github.com/aqlaboratory/QuickBind .
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