2025 AIChE Annual Meeting

(106f) How Large Quantitative Models Are Accelerating Drug Discovery and Delivering New Innovations and Treatment Breakthroughs

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Despite spending an average of $2bn to $4bn in R&D over 10-12 years, nearly 90% of new drugs fail to reach or pass clinical trials. This increased risk drives up the cost of life-saving therapies and discourages research for challenging medical conditions. However, recent advances in AI – in the form of Large Quantitative Models (LQMs) and Quantitative AI simulations – are disrupting drug discovery by significantly decreasing the time, cost and risk of R&D.

This presentation will discuss the impact that LQMs and AI simulation are already having on the biopharma industry, delivering new insights and breakthroughs in Alzheimer’s research when all other advanced computational methods have failed. It will demonstrate how creating digital twins of drug compounds and running billions of simultaneous simulations for efficacy, toxicity, solubility, synthesizability, and other criteria can dramatically reduce the need for costly, time-consuming lab experimentation, while improving hit rates, target identification, property predictions, and more. It will also compare and contrast other AI implementations used in drug discovery, and illuminate innovative new uses for LQMs trained on chemistry data.

The speaker will highlight customer use cases and success stories demonstrating how LQMs have been harnessed to drive innovation and treatment breakthroughs. This session is essential for biopharma executives and researchers looking to understand the full potential of AI and the transformative impact of LQMs in drug discovery and development.