A Next-Generation Drug Discovery Using Artificial Intelligence: Achievements and Challenges
TL;DR
This review explores recent AI innovations—such as AlphaFold for protein structure prediction, generative models for de novo drug design, and graph neural networks for drug repurposing—alongside real-world case studies from companies like Exscientia, Insilico Medicine, and BenevolentAI, which have produced clinical candidates like DSP-1181 and rentosertib.
Abstract
Artificial intelligence is revolutionizing drug discovery by accelerating target identification, molecular design, virtual screening, and toxicity prediction, while tackling longstanding challenges like high costs and lengthy timelines in traditional pipelines. This review explores recent AI innovations—such as AlphaFold for protein structure prediction, generative models for de novo drug design, and graph neural networks for drug repurposing—alongside real-world case studies from companies like Exscientia, Insilico Medicine, and BenevolentAI, which have produced clinical candidates like DSP-1181 and rentosertib. Despite these advances, key hurdles persist, including data quality issues, model interpretability, synthetic feasibility for complex molecules, and integration with experimental workflows, underscoring the need for explainable AI, better datasets, and ethical frameworks to bridge research gaps. Looking ahead, hybrid AI-experimental approaches and collaborations between pharma giants and AI startups promise to deliver safer, more personalized therapies faster.