AI is transforming the pharmaceutical landscape by accelerating drug discovery and clinical trials, potentially reducing costs and improving success rates. The technology impacts various stages, from target identification to post-approval monitoring.
The AI Toolbox: A Range of Technologies
AI encompasses a suite of technologies, including machine learning, deep learning, and generative AI. Generative AI, powered by deep learning, analyzes extensive datasets across scientific literature, genomics, and experimental results to uncover insights that might escape human researchers. These tools enable machines to think and solve problems like humans, creating new content and processing language in sophisticated ways.
How GenAI Accelerates Drug Discovery
Generative AI rapidly explores chemical spaces, generating and evaluating millions of potential molecular structures faster than traditional methods. This expands the range of drug candidates and allows AI models to predict molecular interactions with protein targets, identifying promising compounds and potential side effects early in the development process. Multi-objective optimization capabilities enable AI to simultaneously refine multiple desired properties of a drug, such as its efficacy, safety, and bioavailability.
Currently, AI is most commonly used in small molecule drug discovery, drug target discovery, and drug molecule design in oncology and CNS. Companies of all sizes are actively utilizing AI as point solutions or as fully AI-embedded end-to-end approaches. These AI models continue to grow in size and complexity, and require increasingly advanced computational resources, including high-end GPUs that support GenAI, to process proprietary datasets securely.
AI-Focused Biotechs: Increasing Research Speed
Several AI-discovered drug candidates have entered Phase 2 and Phase 3 clinical trials in record time. For example, DSP-1181, developed by Exscientia in collaboration with Sumitomo Dainippon Pharma for obsessive-compulsive disorder, had a discovery phase of only 12 months. Insilico Medicine identified new drug targets and generated candidate molecules in just 18 months. BenevolentAI leveraged its AI platform to identify baricitinib as a potential treatment for COVID-19 in just three days. Verge Genomics advanced VRG50635, a small molecule inhibitor of PIKfyve for amyotrophic lateral sclerosis (ALS), from research to clinic in just four years, successfully moving past Phase I trials. Many AI-enabled drug discovery programs now take less than four years to complete, compared to the typical five years or more required for the traditional discovery phase.
Increasing Speed With AI In Clinical Development
AI is also revolutionizing clinical trials by automating tasks, improving analytics, and enhancing predictive modeling. A recent FDA workshop highlighted AI's potential in trial optimization. A case study with Janssen Pharmaceuticals demonstrated that interpretable AI predicted incidence rates at potential sites for their COVID-19 vaccine trial, resulting in 33% faster trials and 25% fewer participants. AI tools are also enhancing trial operations, monitoring treatment response, improving patient engagement, and accelerating recruitment. In regulatory affairs, AI tracks global guidances and predicts queries, compounding the speed improvements already seen in AI-assisted drug design.
AI In Drug Discovery And Development: Faster…Cheaper…Better Value?
AI-driven drug discovery is on the rise. Automating data analysis and predicting outcomes with greater accuracy result in drug candidates entering clinical trials in record time compared to traditional drug development. Beyond speed, AI promises to reduce costs by minimizing experimental overhead and improving trial efficiency. This would not only cut financial burdens but also improve the quality of drug candidates, balancing drug properties like safety and efficacy, and increasing the chances of approval and success in the market.
