Revolutionizing new drug discovery: Harnessing AI and machine learning to overcome traditional challenges and accelerate targeted therapies

Designing highly targeted, selective drugs with desirable absorption, distribution, metabolism, excretion, and pharmacokinetic (PK) profiles; single-digit nanomolar efficacy; and a wider therapeutic index are challenging. In the traditional drug discovery process, researchers screen thousands of chemical compounds during pre-clinical development, progressing through hit identification, lead optimization, and candidate selection to shortlist – potential clinical candidates with favorable PK profiles, high tolerability, and manageable toxicity. The selected candidate must demonstrate sufficient efficacy in treating the target disease in humans. Despite these efforts, the success rate of the pre-clinical candidate to sail through Phase I, Phase II, and Phase III in clinical trials remains exceedingly low. Supported by powerful data-driven tools, artificial intelligence (AI) has transformed this traditional drug discovery process by enabling the analysis of large quantities of omics, phenotypic, and expression data to identify the biological mechanisms of pathological conditions and in turn identify druggable proteins or genes. The generative AI-powered toolbox creates novel compounds from scratch, assists scientists in optimizing druggability attributes, and bridges the differences between animal and human physiology and anatomy to predict potential toxicity in humans with high accuracy. This review discusses the bottlenecks in the traditional drug discovery approach, the impact of AI and machine learning (ML) in drug discovery, and potential challenges associated with AI/ML adoption.