An introduction to GitHub and its significance for AI-driven drug discovery

GitHub has become essential to AI-driven drug discovery by facilitating code sharing, collaboration, and reproducible workflows. As more tools for screening, modeling, and data-driven decision-making are hosted on GitHub, researchers need clear, rigorous methods to identify, evaluate, and reuse repositories in a scientifically robust manner.

In this report, the authors summarize GitHub concepts most relevant to research practice (e.g. repositories, documentation, licensing, releases, testing, and archiving) and propose a practical framework for navigating drug-discovery repositories. The report incorporates a Scopus trend analysis from 2013 to 2024 using a GitHub-focused search with drug discovery keywords, as well as keyword-based counts of GitHub repositories in selected categories to show topic density.

GitHub repositories should be recognized as peer-assessable research outputs in AI-driven drug discovery rather than as supplementary material. At a minimum, this means clear documentation of intended use and limitations, a pinned environment or container for reproducibility, an explicit license, and automated testing via continuous integration. In addition, enhanced validation and governance are necessary to bridge exploratory research code with translational and industrial reliability expectations.