42 TWENTYFOURSEVENBIOPHARMA Issue 2 / June 2025 Editorial team 24/7 BIOPHARMA Addressing these challenges will be critical to unlocking the full potential of AI in drug discovery and ensuring that its benefits are broadly realized. The next frontier in drug discovery As AI technology continues to advance, the pharmaceutical industry is on the cusp of a major transformation. AI’s ability to rapidly analyze vast datasets, including genomic, phenotypic and molecular interaction data, is already accelerating drug discovery. This computational power can streamline the preclinical phase, traditionally one of the most time-consuming and costly parts of drug development, by quickly identifying promising compounds and predicting their likely efficacy. In parallel, AI can also support personalized medicine by identifying patient subgroups most likely to respond to specific therapies, reducing trial-anderror approaches and enhancing overall treatment outcomes. Together, these capabilities promise to not only bring therapies to market faster but also reduce costs, making cutting-edge treatments more accessible to a broader range of patients. Beyond these immediate efficiencies, the impact of AI is likely to extend far beyond large pharmaceutical companies. As AI tools become more accessible, smaller biotech firms, academic research centers and emerging startups are gaining the ability to innovate at unprecedented speed, contributing to a more diverse and dynamic life sciences ecosystem. This democratization of technology can drive broader collaboration, empower new scientific discoveries, and ultimately expand the boundaries of what is possible in drug discovery. Realizing this potential will take more than just implementing new tools. It demands that organizations evolve by investing in data infrastructure, encouraging interdisciplinary collaboration and embedding ethics and patient focus throughout.. Building adaptable teams with a strong grasp of both science and data will help companies stay competitive as the industry changes. Ultimately, AI holds the potential to not only enhance existing drug discovery practices but to fundamentally change how new therapies are conceptualized, developed and delivered. Those who strategically embrace AI will be best positioned to lead this next wave of pharmaceutical innovation. Seizing the AI advantage in drug discovery The companies that move early on AI will be best placed to lead the field. However, this shift goes beyond simply integrating new tools and algorithms. It will require long-term planning, strong data foundations and a mindset that prioritises transparency and trust. These principles will be essential as the industry adopts more AI-led approaches. For those looking to stay ahead of these trends, engaging with thought leaders and innovators in the AI space will be crucial. Events like CPHI Americas 2025 has recently offered a unique platform to connect, learn and collaborate, providing valuable insights into the future of AI-driven drug discovery. As the industry gathers to discuss the next wave of pharmaceutical innovation at CPHI Frankfurt later this year, AI will undoubtedly be a theme, promising to redefine the possibilities of drug development for decades to come. References 1. Oxford Drug Discovery Institute. AI-powered databases boost Alzheimer’s drug discovery process. Wall Street Journal. 2025. Available at: https://www.wsj.com/articles/ai-powereddatabases-boost-the-alzheimers-drug-discoveryprocess-b9b75180. Accessed May 12, 2025. 2. Stokes JM, Yang K, Swanson K, et al. A deep learning approach to antibiotic discovery. Cell. 2020;180(4):688-702.e13. https://pubmed.ncbi. nlm.nih.gov/32084340/. 3. Zhang Y, et al. AlphaFold accelerates AI-powered drug discovery. arXiv preprint. 2022. https://arxiv. org/abs/2201.09647. 4. Immunai signs $18M collaboration with AstraZeneca to make cancer drug trials more efficient. Ctech. September 26, 2024. Available at: https://www.calcalistech.com/ctechnews/article/ syx7my7a0 5. Considerations for the Use of Artificial Intelligence To Support Regulatory DecisionMaking for Drug and Biological Products. U.S. Food and Drug Administration. Available at: https://www.fda.gov/regulatory-information/ search-fda-guidance-documents/considerationsuse-artificial-intelligence-support-regulatorydecision-making-drug-and-biological 6. https://pmc.ncbi.nlm.nih.gov/articles/ PMC10302890/ 7. https://pubmed.ncbi.nlm.nih.gov/37105727/ ARTIFICIAL INTELLIGENCE
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