ArticleBioinformatics (Oxford, England)2024
OpenDock: a pytorch-based open-source framework for protein-ligand docking and modelling.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Enhanced Line Search Improves Robustness and Efficiency of Pose Sampling in Protein-Ligand Docking.Journal of chemical theory and computation · 2026Article
- Integrative machine learning approaches for enzyme kinetic parameter prediction.Briefings in bioinformatics · 2026Review
- Open-Source Molecular Docking and AI-Augmented Structure-Based Drug Design: Current Workflows, Challenges, and Opportunities.International journal of molecular sciences · 2026Review
- PepLM-GNN: A graph neural network framework leveraging pre-trained language models for peptide-protein binding prediction.PLoS computational biology · 2026Article
- Assessing the potential of deep learning for protein-ligand docking.Nature machine intelligence · 2026Article
- The covalent docking software landscape: features and applications in drug design.Briefings in bioinformatics · 2025Review
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Authors and funding
9 authors.
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Abstract
motivationMolecular 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.
resultsTo 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. AVAILABILITY AND IMPLEMENTATION: OpenDock is publicly available at: https://github.com/guyuehuo/opendock.
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