McNutt Andrew T, Li Yanjing, Meli Rocco, Aggarwal Rishal, Koes David Ryan
Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.
Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.
J Cheminform. 2025 Mar 2;17(1):28. doi: 10.1186/s13321-025-00973-x.
Computer-aided drug design has the potential to significantly reduce the astronomical costs of drug development, and molecular docking plays a prominent role in this process. Molecular docking is an in silico technique that predicts the bound 3D conformations of two molecules, a necessary step for other structure-based methods. Here, we describe version 1.3 of the open-source molecular docking software GNINA. This release updates the underlying deep learning framework to PyTorch, resulting in more computationally efficient docking and paving the way for seamless integration of other deep learning methods into the docking pipeline. We retrained our CNN scoring functions on the updated CrossDocked2020 v1.3 dataset and introduce knowledge-distilled CNN scoring functions to facilitate high-throughput virtual screening with GNINA. Furthermore, we add functionality for covalent docking, where an atom of the ligand is covalently bound to an atom of the receptor. This update expands the scope of docking with GNINA and further positions GNINA as a user-friendly, open-source molecular docking framework. GNINA is available at https://github.com/gnina/gnina .Scientific contributions: GNINA 1.3 is an open source a molecular docking tool with enhanced support for covalent docking and updated deep learning models for more effective docking and screening.
计算机辅助药物设计有潜力显著降低药物研发的巨额成本，而分子对接在此过程中发挥着重要作用。分子对接是一种计算机模拟技术，可预测两个分子的结合三维构象，这是其他基于结构的方法的必要步骤。在此，我们介绍开源分子对接软件GNINA的1.3版本。此版本将底层深度学习框架更新为PyTorch，从而实现更高效的对接计算，并为将其他深度学习方法无缝集成到对接流程中铺平道路。我们在更新后的CrossDocked2020 v1.3数据集上重新训练了我们的卷积神经网络评分函数，并引入了知识蒸馏的卷积神经网络评分函数，以促进使用GNINA进行高通量虚拟筛选。此外，我们添加了共价对接功能，其中配体的一个原子与受体的一个原子共价结合。此更新扩展了GNINA的对接范围，并进一步将GNINA定位为一个用户友好的开源分子对接框架。可在https://github.com/gnina/gnina获取GNINA。科学贡献：GNINA 1.3是一个开源分子对接工具，增强了对共价对接的支持，并更新了深度学习模型，以实现更有效的对接和筛选。
