Artificial intelligence and machine learning, a game changer in pharmaceutical development? – An updated perspective arising from Journees Galeniques 2024

Information from the abstract

Artificial intelligence (AI) and machine learning (ML) are increasingly being applied across pharmaceutical development, from drug-property prediction and safety assessment to formulation design, model-informed development and clinical research. This perspective synthesizes discussions from Journées Galéniques 2024 and updates them in light of scientific and regulatory developments through 2026. ML enables prediction, pattern recognition, and data-driven decision-making, helping researchers identify drug targets, design new molecules, predict toxicity, and optimize drug properties. In formulation development, ML can improve solubility prediction, excipient selection, and formulation strategy by reducing trial-and-error approaches. AI-based models and imaging technologies can also enhance understanding of drug delivery systems and improve prediction of in vivo performance. However, much of the published literature remains retrospective and is based on small or heterogeneous datasets, with limited external, prospective or clinically relevant validation. Recent regulatory frameworks place increasing emphasis on a clearly defined context of use, risk-based assessment of model credibility, data governance, generalizability, human oversight and lifecycle management. AI and ML should therefore be viewed not as replacements for pharmaceutical science, but as increasingly powerful tools that can strengthen and accelerate it. Their broader impact will depend on high-quality, interoperable data, rigorous validation, integration with pharmaceutical and clinical expertise, and clear evidence of meaningful clinical, manufacturing or regulatory value.

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Related topics: Computational Drug Discovery Methods · Artificial Intelligence in Healthcare and Education · Machine Learning in Materials Science