In general, flexible ligand docking is used for docking simulations under the premise that the position of the binding site is already known, and meanwhile it can also be used without prior knowledge of the binding site. However, most of the optimization search algorithms used in popular docking software are far from being ideal in the first case, and they can hardly be directly utilized for the latter case due to the relatively large search area. In order to design an algorithm that can flexibly adapt to different sizes of the search area, we propose an effective swarm intelligence optimization algorithm in this paper, called diversity-controlled Lamarckian quantum particle swarm optimization (DCL-QPSO). The highlights of the algorithm are a diversity-controlled strategy and a modified local search method. Integrated with the docking environment of Autodock, the DCL-QPSO is compared with Autodock Vina, Glide and other two Autodock-based search algorithms for flexible ligand docking. Experimental results revealed that the proposed algorithm has a performance comparable to those of Autodock Vina and Glide for dockings within a certain area around the binding sites, and is a more effective solver than all the compared methods for dockings without prior knowledge of the binding sites.

中文翻译：

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一种有效的群体智能优化算法，用于灵活的配体对接


一般来说，柔性配体对接是在已知结合位点位置的前提下进行对接模拟，同时也可以在不事先知道结合位点的情况下使用。然而，目前流行的对接软件中使用的大多数优化搜索算法在第一种情况下远远不够理想，并且由于搜索区域相对较大，很难直接用于第二种情况。为了设计一种能够灵活适应不同大小搜索区域的算法，本文提出了一种有效的群体智能优化算法，称为多样性控制拉马克量子粒子群优化（DCL-QPSO）。该算法的亮点是多样性控制策略和改进的局部搜索方法。与Autodock的对接环境集成，将DCL-QPSO与Autodock Vina、Glide等两种基于Autodock的灵活配体对接搜索算法进行比较。实验结果表明，该算法在结合位点周围一定区域内的对接方面具有与 Autodock Vina 和 Glide 相当的性能，并且是比所有对比的无需先验知识结合位点的对接方法更有效的求解器。