aceAI-enabled drug and molecular discovery: computational methods, platforms, and translational horizons
The integration of artificial intelligence (AI) with bioinformatics has initiated a transformative shift in drug discovery, redefining how pharmaceutical research and development are conducted. This review examines both the current state and emerging prospects of AI-driven strategies across the drug discovery pipeline, from target identification and molecular design to clinical applications. Advances in machine learning, deep learning, graph neural networks, transformers, foundation models, and quantum computing…
AI integration in drug discovery still limited by data quality and bias, lack of transparency and interpretability, high computational demands, and privacy and fairness risks.
Review notes persistent barriers to translation including data quality and bias, model interpretability, compute demands, and privacy and fairness concerns.
Evidence
- Peer-reviewedDiscover Molecules2025-12-12
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Truvace Impact Record TRV-2026-0414, v1: “AI-enabled drug and molecular discovery: computational methods, platforms, and translational horizons.” Truvace, 2026-07-20. /record/TRV-2026-0414 (accessed at citation time). sha256 03991281556f4509…
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