Predicting how a drug-like molecule binds to a specific protein target is a core problem in drug discovery. An extremely fast computational binding method would enable key applications such as fast virtual screening or drug engineering. Existing methods are computationally expensive as they rely on heavy candidate sampling coupled with scoring, ranking, and fine-tuning steps. We challenge this paradigm with EquiBind, an SE(3)-equivariant geometric deep learning model performing direct-shot prediction of both i) the receptor binding location (blind docking) and ii) the ligand's bound pose and orientation. EquiBind achieves significant speed-ups and better quality compared to traditional and recent baselines. Further, we show extra improvements when coupling it with existing fine-tuning techniques at the cost of increased running time. Finally, we propose a novel and fast fine-tuning model that adjusts torsion angles of a ligand's rotatable bonds based on closed-form global minima of the von Mises angular distance to a given input atomic point cloud, avoiding previous expensive differential evolution strategies for energy minimization.
翻译:预测药物类分子如何与特定蛋白质目标相关是药物发现的一个核心问题。极快的计算约束方法将使得快速虚拟筛选或药物工程等关键应用得以实现。 现有的方法在计算上成本高昂, 因为它们依赖重候选取样, 加上评分、 排名和微调步骤。 我们用EquiBind这个SE(3)-QQQQQQQViet 深层次学习模型挑战这一模式,该模型直接预测了i) 受体绑定地点( 盲对接) 和 离子体的捆绑姿态和方向。 EquiBind 与传统和近期的基线相比, 能够取得显著的加速和质量。 此外, 在以更高的运行时间成本与现有微调技术结合时, 我们展示了额外的改进。 最后, 我们提出一个新颖的快速微调模型, 以适应离子的螺旋形螺旋形形全球微距离与特定输入原子点云的封闭式微距离( ) 为基础, 来调整结。