review · ChemBioChem
Artificial intelligence is changing pharmaceutical research by merging advanced computational methods with traditional scientific discovery. Computational tools now assist at numerous stages of drug development, offering significant progress in molecular design, chemical synthesis planning, polypharmacology, and the repurposing of existing medicines. These methods are also increasingly applied to predict critical drug characteristics, including bioactivity, physicochemical traits, and potential toxicity, helping to address enduring research obstacles. However, the adoption of these technologies faces notable constraints. Key challenges include issues with underlying data quality, limited model generalisability, high computational requirements, and ethical concerns. Overcoming these technical and practical hurdles remains essential for research teams seeking to capture the full benefits of computational approaches across modern development pipelines.
Developing new medicines has traditionally been slow, expensive, and technically difficult. Artificial intelligence provides computational tools to design molecules, predict their safety, and identify alternative uses for existing treatments. Understanding both the capabilities and the technical limitations of these methods helps researchers and organisations apply digital tools more effectively to complex healthcare problems.
The work maps computational applications across the pharmaceutical development pipeline, of direct interest to biotechnology companies, pharmaceutical developers, and research organisations. While computational tools are already applied to design molecules, predict properties, and repurpose drugs, the review highlights ongoing challenges in data quality, generalisability, and computing requirements, indicating that technical integration across discovery pipelines remains an evolving, ongoing process.
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The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.
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DOI: 10.1002/cbic.202300816
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