Protein-ligand docking programs are routinely used in structure-based drug design to find the optimal binding pose of a ligand in the protein’s active site. These programs are also used to identify potential drug candidates by ranking large sets of compounds. As more accurate and efficient docking programs are always desirable, constant efforts focus on developing better docking algorithms or improving the scoring function. Recently, chaotic maps have emerged as a promising approach to improve the search behavior of optimization algorithms in terms of search diversity and convergence speed. However, their effectiveness on docking applications has not been explored. Herein, we integrated five popular chaotic maps—logistic, Singer, sinusoidal, tent, and Zaslavskii maps—into PSOVina $$^{{\mathrm{2LS}}}$$ , a recent variant of the popular AutoDock Vina program with enhanced global and local search capabilities, and evaluated their performances in ligand pose prediction and virtual screening using four docking benchmark datasets and two virtual screening datasets. Pose prediction experiments indicate that chaos-embedded algorithms outperform AutoDock Vina and PSOVina in ligand pose RMSD, success rate, and run time. In virtual screening experiments, Singer map-embedded PSOVina $$^{{\mathrm{2LS}}}$$ achieved a very significant five- to sixfold speedup with comparable screening performances to AutoDock Vina in terms of area under the receiver operating characteristic curve and enrichment factor. Therefore, our results suggest that chaos-embedded PSOVina methods might be a better option than AutoDock Vina for docking and virtual screening tasks. The success of chaotic maps in protein-ligand docking reveals their potential for improving optimization algorithms in other search problems, such as protein structure prediction and folding. The Singer map-embedded PSOVina $$^{{\mathrm{2LS}}}$$ which is named PSOVina-2.0 and all testing datasets are publicly available on https://cbbio.cis.umac.mo/software/psovina .

中文翻译：

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用于蛋白质-配体对接和虚拟筛选的混沌嵌入式粒子群优化方法。

蛋白质-配体对接程序通常用于基于结构的药物设计中，以找到蛋白质活性位点中配体的最佳结合姿势。这些程序还用于通过对大量化合物进行排名来识别潜在的候选药物。由于始终需要更准确和有效的对接程序，因此，不断的努力将重点放在开发更好的对接算法或改善评分功能上。最近，混沌图谱已成为一种有前途的方法，可以从搜索多样性和收敛速度上改善优化算法的搜索行为。但是，尚未探讨它们在对接应用程序上的有效性。在这里，我们整合了五种流行的混沌地图-后勤，歌手，正弦曲线，帐篷，和Zaslavskii映射到PSOVina $$ ^ {{{\ mathrm {2LS}}} $$中，这是流行的AutoDock Vina程序的最新变体，具有增强的全局和局部搜索功能，并使用以下方法评估了它们在配体姿态预测和虚拟筛选中的性能四个对接基准数据集和两个虚拟筛选数据集。姿势预测实验表明，在配体姿势RMSD，成功率和运行时间方面，混沌嵌入算法的性能优于AutoDock Vina和PSOVina。在虚拟筛选实验中 嵌入歌手图的PSOVina $$ ^ {{\ mathrm {2LS}}} $$实现了非常明显的五到六倍的加速，在接收器工作特性曲线和富集因子下的面积方面，其筛选性能与AutoDock Vina相当。因此，我们的结果表明，对于停靠和虚拟筛选任务而言，与AutoDock Vina相比，混沌嵌入式PSOVina方法可能是更好的选择。蛋白质配体对接中混沌图谱的成功揭示了其在其他搜索问题（如蛋白质结构预测和折叠）中改进优化算法的潜力。