Engineering · 2023 · 118 citations · 326 references
PharmacyPharmacologyDrug DesignDrug DiscoveryPharmaceutical TechnologyComputational Drug DiscoveryDrug-target Interaction MappingComputer-aided Drug DiscoveryEarly Drug DiscoveryHigh-dimensional Data Mining
Drug discovery and development profoundly affect human health and the pharmaceutical market, but the long, complex R&D process often leaves investments unrewarded, and the recent explosion of biomedical data and advances in AI have revolutionized drug R&D by enabling more efficient, lower‑cost drug discovery. The review aims to overview common AI models in drug discovery and examine their detailed applications across all stages of R&D, from target identification to market impact. The authors review AI models and their applications across drug‑R&D stages, then analyze major limitations and propose solutions. AI has revolutionized drug R&D by enabling more efficient, lower‑cost drug discovery thanks to big‑data analytics.
Drug discovery and development affects various aspects of human health and dramatically impacts the pharmaceutical market. However, investments in a new drug often go unrewarded due to the long and complex process of drug research and development (R&D). With the advancement of experimental technology and computer hardware, artificial intelligence (AI) has recently emerged as a leading tool in analyzing abundant and high-dimensional data. Explosive growth in the size of biomedical data provides advantages in applying AI in all stages of drug R&D. Driven by big data in biomedicine, AI has led to a revolution in drug R&D, due to its ability to discover new drugs more efficiently and at lower cost. This review begins with a brief overview of common AI models in the field of drug discovery; then, it summarizes and discusses in depth their specific applications in various stages of drug R&D, such as target discovery, drug discovery and design, preclinical research, automated drug synthesis, and influences in the pharmaceutical market. Finally, the major limitations of AI in drug R&D are fully discussed and possible solutions are proposed.
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