A curated list of resources for molecular docking, protein-protein docking, and related computational biology tasks.
Awesome-Molecular-Docking is a curated collection of resources focused on computational molecular docking and related tasks in structural biology and drug discovery. It aggregates datasets, software tools, and research papers to help researchers and developers navigate the field efficiently. The repository covers areas like molecule-protein docking, protein-protein docking, antibody design, and molecular dynamics simulation.
Computational biologists, bioinformaticians, drug discovery researchers, and machine learning practitioners working on protein-ligand interactions or structural biology problems. It's particularly useful for those entering the field or looking for up-to-date tools and datasets.
It saves significant time by providing a centralized, organized, and community-vetted list of resources instead of requiring manual literature and tool discovery. The focus on recent machine-learning approaches and open-source tools makes it especially valuable for modern computational biology workflows.
We would like to maintain a list of resources which aim to solve molecular docking and other closely related tasks.
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Emphasizes peer-reviewed papers and open-source code, as shown in sections like 'Molecule-Protein Docking' with links to recent arXiv preprints and GitHub repositories, ensuring high-quality, verifiable resources.
Resources are clearly grouped into domains such as Dataset, Software for Docking, and Antibody Design, making it easy to navigate specific subfields without sifting through unstructured lists.
Actively welcomes contributions via pull requests and email contact, as indicated by PR badges and contribution guidelines, helping the list stay current with community input and advancements.
Includes recent publications from 2021-2022, covering cutting-edge methods like diffusion models and geometric deep learning in docking, which is crucial for fast-moving research areas.
Provides only links and brief citations without comparisons, evaluations, or tutorials, leaving users to assess resource quality and suitability on their own, which can be time-consuming.
As a manually updated repository, it may not capture the very latest developments compared to automated databases or feeds, potentially missing emerging tools or papers.
All resources point to external sites; broken links or inaccessible content can reduce utility, and there's no built-in archiving or mirroring to ensure long-term accessibility.