ABSTRACT Artificial intelligence (AI) is reshaping drug discovery by accelerating timelines and reducing costs, yet its impact remains constrained by a persistent gap between computational promise and translational delivery. This gap stems from upstream preclinical failures, including weak target validation, biologically irrelevant models, and insufficient accountability for overstated methodological claims that contribute to late‐stage attrition. The Implementation, Methodology, Productivity, Assessment, Collaboration, Translation (IMPACT) framework addresses these root causes by establishing global standards that reinforce biological grounding, methodological credibility, and equitable collaboration. Implementation emphasizes Findable, Accessible, Interoperable, and Reusable (FAIR)‐compliant datasets, standardized vocabularies, and clear gradients of AI involvement from assisted to fully AI‐driven workflows. Methodology prioritizes reproducibility through model cards, containerized environments, and transparent reporting to support robust models. Productivity aligns AI efforts with urgent therapeutic priorities, including rare diseases, antimicrobial resistance, drug repurposing, and natural‐product discovery. Assessment promotes rigorous benchmarking, blind validation, and uncertainty quantification, drawing on the long‐established CASP model as a historical gold standard while critically examining emerging initiatives such as CACHE and Polaris Hub, which remain comparatively recent and evolving. Collaboration leverages federated learning, pre‐competitive consortia, and interdisciplinary teams integrating AI specialists with domain experts. Translation ensures outputs are explainable, clinically relevant, ethically aligned, and regulatory‐ready, consistent with emerging frameworks such as the FDA Draft Guidance on AI in Drug Development and the EU AI Act. By integrating technical standards with operational governance mechanisms, IMPACT provides a structured pathway toward transparent and translationally reliable AI‐driven drug discovery. This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Software > Molecular Modeling Data Science > Chemoinformatics
Gangwal et al. (Sun,) studied this question.