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<p>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 <i>in silico</i> 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 <span>Gnina</span>. 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 <span>Gnina</span>. 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 <span>Gnina</span> and further positions <span>Gnina</span> as a user-friendly, open-source molecular docking framework. <span>Gnina</span> is available at <a href="https://github.com/gnina/gnina">https://github.com/gnina/gnina</a>.</p><p><b>Scientific contributions</b>: GNINA 1.3 is an open source&#xa0;a molecular docking tool with enhanced support for covalent docking and updated deep learning models for more effective docking and screening.</p>

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GNINA 1.3: the next increment in molecular docking with deep learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月2日

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  • Andrew T. McNutt,
  • Yanjing Li,
  • Rocco Meli,
  • Rishal Aggarwal,
  • David Ryan Koes

摘要

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.