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APAC takes Centre stage in Al-Driven Drug Discovery
BioSpectrum Asia
|BioSpectrum Asia Feb 2025
The Asia-Pacific (APAC) region is rapidly emerging as a leader in Al-driven drug discovery, fuelled by robust research ecosystems and strong industry collaboration.

Substantial investments, cutting-edge technologies, and strategic partnerships are optimising drug development efficiency. As Artificial Intelligence (AI) has become more embedded into many facets of everyday life, it has become an indispensable part of the drug discovery landscape. 2024 was the year of reckoning when Al in drug discovery received the highest validation—three researchers who leveraged the neural network Al programme AlphaFold to predict protein structures won the Nobel Prize in Chemistry. The industry is pushing ahead, using tech to make the business of drug development faster, safer, and more economical. Morgan Stanley estimates that Al's use in drug development could lead to 50 more successful treatments over a decade, potentially creating a $50 billion market. Let's explore the region's efforts in accelerating drug development through Al.
Drug discovery is a lengthy and costly process, typically requiring billions of dollars and nearly a decade to bring a drug to market. AI holds the promise of reducing both the costs and the timelines involved in this process. A study published by the Boston Consulting Group (BCG) found that at least 75 drug candidates developed by companies using AI in their discovery process were in clinical trials over the past decade. Impressively, 80–90 percent of the candidates that entered phase I clinical testing were successful, a success rate considerably higher than the industry average of 50–60 percent.
AI research in drug discovery primarily focuses on small molecules, with oncology as the leading area due to the growing global cancer burden. Immunology follows, with 21 percent of companies using AI for immunological treatments. Fewer programmes target neurology and inflammation, according to Deep Pharma Intelligence.
Big pharma and AI
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