40 TWENTYFOURSEVENBIOPHARMA Issue 2 / June 2025 ARTIFICIAL INTELLIGENCE Artificial intelligence (AI) is rapidly transforming the pharmaceutical landscape, fundamentally changing how drugs are discovered, developed and brought to market. Once a niche technology, AI has become a critical enabler of innovation, significantly reducing the time and cost of drug discovery while minimizing the risks traditionally associated with the process. From early-stage target identification to clinical trial optimization, AI is revealing insights that were previously out of reach, helping researchers overcome long-standing bottlenecks in drug development. As the pharmaceutical industry grapples with rising R&D costs, complex regulatory landscapes and growing demand for patient-centric therapies, AI offers a powerful path forward. It allows researchers to explore vast chemical spaces more efficiently, accelerate candidate screening and improve the precision of clinical trials, ultimately increasing the likelihood of successful drug approvals. Against this backdrop, this article will examine the current landscape of AI in drug discovery, the trends shaping its future and the challenges that remain, offering a glimpse into the exciting ossibilities that lie ahead. AI’s growing influence AI is becoming an increasingly important part of modern drug discovery, transforming each stage of the R&D process through data-driven insights and computational power. Its influence can span from early-stage target identification to precision medicine, fundamentally changing how therapies are developed. Identifying the right molecular targets is a critical early step in drug development, as these targets form the foundation for effective therapies. AI algorithms can rapidly sift through vast genomic, transcriptomic and proteomic datasets to pinpoint potential targets linked to specific diseases. These approaches have already allowed for AI-powered databases to accelerate Alzheimer’s drug discovery, reducing the evaluation time of 54 immune-related genes from weeks to days by using knowledge graphs — a technology that maps complex relationships between data points, revealing previously hidden connections more efficiently1. Once promising targets are identified, the next challenge is designing molecules that can effectively interact with them. This is where AI truly shines, as it can predict molecular structures and behaviors before they are synthesized in the lab, significantly speeding
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