Artificial intelligence in drug design: why a 'one-size-fits-all' approach remains out of reach

Advances in artificial intelligence (AI) have transformed the drug design and discovery process, introducing novel methods that can reduce costs, increase success rates, and shorten development timelines. However, due to the complexity and multifactorial nature of this process, no single AI approach is likely to be universally effective.

This review summarizes progress made over the past five years toward diverse drug development goals using AI tools. It also discusses the main challenges that inhibit the development and adoption of a broad AI solution in this field.

Despite major advancements, AI fails to reach its full potential due to issues related to data quality, model complexity, computational costs, and organizational barriers. At present, the effectiveness of any AI approach heavily depends on its application. Ultimately, while the world strives for a general-purpose AI, no method in drug discovery can yet be considered universally applicable, and rather than relying on a one-size-fits-all solution, individual trade-offs and research objectives need to be carefully aligned to harness AI's potential in drug discovery.