Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518000, China.
University of Chinese Academy of Sciences, Beijing, 100049, China.
Bioinformatics. 2024 Nov 1;40(11). doi: 10.1093/bioinformatics/btae628.
Molecular docking is an invaluable computational tool with broad applications in computer-aided drug design and enzyme engineering. However, current molecular docking tools are typically implemented in languages such as C++ for calculation speed, which lack flexibility and user-friendliness for further development. Moreover, validating the effectiveness of external scoring functions for molecular docking and screening within these frameworks is challenging, and implementing more efficient sampling strategies is not straightforward.
To address these limitations, we have developed an open-source molecular docking framework, OpenDock, based on Python and PyTorch. This framework supports the integration of multiple scoring functions; some can be utilized during molecular docking and pose optimization, while others can be used for post-processing scoring. In terms of sampling, the current version of this framework supports simulated annealing and Monte Carlo optimization. Additionally, it can be extended to include methods such as genetic algorithms and particle swarm optimization for sampling docking poses and protein side chain orientations. Distance constraints are also implemented to enable covalent docking, restricted docking or distance map constraints guided pose sampling. Overall, this framework serves as a valuable tool in drug design and enzyme engineering, offering significant flexibility for most protein-ligand modelling tasks.
OpenDock is publicly available at: https://github.com/guyuehuo/opendock.
分子对接是一种非常有价值的计算工具，在计算机辅助药物设计和酶工程中有广泛的应用。然而，目前的分子对接工具通常使用 C++等语言来实现计算速度，这对于进一步开发缺乏灵活性和用户友好性。此外，在这些框架内验证外部评分函数对分子对接和筛选的有效性具有挑战性，并且实现更有效的采样策略并不简单。
为了解决这些限制，我们基于 Python 和 PyTorch 开发了一个开源的分子对接框架 OpenDock。该框架支持多种评分函数的集成；有些可以在分子对接和构象优化过程中使用，而其他的则可以用于后处理评分。在采样方面，这个框架的当前版本支持模拟退火和 Monte Carlo 优化。此外，它可以扩展到包括遗传算法和粒子群优化等方法，用于采样对接构象和蛋白质侧链构象。还实现了距离约束，以支持共价对接、受限对接或距离图约束引导构象采样。总的来说，这个框架是药物设计和酶工程中的一个有价值的工具，为大多数蛋白质配体建模任务提供了很大的灵活性。
OpenDock 可在以下网址获得：https://github.com/guyuehuo/opendock。
