Treyde Wojtek, Bouatta Nazim, Kim Seohyun Chris, AlQuraishi Mohammed
Department of Systems Biology, Columbia University, New York, NY.
Department of Systems Biology, Harvard Medical School, Boston, MA.
ArXiv. 2024 Oct 21:arXiv:2410.16474v1.
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 this GitHub repository.
预测配体与靶蛋白的结合构象是早期计算机辅助药物发现的关键组成部分。机器学习方法的最新进展主要集中在以模型运行时间为代价来提高构象质量。对于高通量虚拟筛选应用而言，这暴露出了一个能力缺口，而适度准确但快速的构象预测可以填补这一缺口。为此，我们开发了QuickBind，一种轻量级的构象预测算法。我们在广泛使用的基准上评估了QuickBind，发现它在模型准确性和运行时间之间提供了一个有吸引力的权衡。为了促进虚拟筛选应用，我们用一个结合亲和力模块增强了QuickBind，并展示了它对多个临床相关药物靶点的能力。最后，我们研究了QuickBind进行预测的机制基础，发现它已经学习到了分子对接的关键物理化学性质，为机器学习模型如何生成蛋白质-配体构象提供了新的见解。凭借其简单性，QuickBind既可以作为一种有效的虚拟筛选工具，也可以作为探索新模型架构和创新的最小测试平台。模型代码和权重可在这个GitHub仓库中获取。
