The generation of small molecule candidate (ligand) binding poses in its target protein pocket is important for computer-aided drug discovery. Typical rigid-body docking methods ignore the pocket flexibility of protein, while the more accurate pose generation using molecular dynamics is hindered by slow protein dynamics. We develop a tiered tensor transform (3T) algorithm to rapidly generate diverse protein-ligand complex conformations for both pose and affinity estimation in drug screening, requiring neither machine learning training nor lengthy dynamics computation, while maintaining both coarse-grain-like coordinated protein dynamics and atomistic-level details of the complex pocket. The 3T conformation structures we generate achieve significantly higher accuracy in active ligand classification than traditional ensemble docking using hundreds of experimental protein conformations. Furthermore, we demonstrate that 3T can be used to explore distant protein-ligand binding poses within the protein pocket. 3T structure transformation is decoupled from the system physics, making future usage in other computational scientific domains possible.

翻译：小分子候选配体在其靶蛋白口袋中的结合姿态生成对计算机辅助药物发现至关重要。传统的刚性对接方法忽略了蛋白质口袋的柔性，而利用分子动力学进行更精确的姿态生成则受限于缓慢的蛋白质动力学过程。我们开发了一种分级张量变换（3T）算法，可快速生成多样化的蛋白质-配体复合物构象，用于药物筛选中的姿态评估和亲和力预测。该方法既无需机器学习训练，也无需冗长的动力学计算，同时能保持粗粒化级别的协同蛋白质动力学特性及复合物口袋的原子级细节。通过3T生成的构象结构在活性配体分类中的准确率显著高于使用数百种实验蛋白质构象的传统集成对接方法。此外，我们证明3T可用于探索蛋白质口袋内远距离的蛋白质-配体结合姿态。3T结构变换与系统物理特性解耦，使其未来可应用于其他计算科学领域。