Mathew Martin

AI is rapidly advancing toxicology research, but its true value lies in augmenting human expertise, improving data usage, and accelerating learning curves for new modalities. This facilitated discussion will explore where AI produces real gains today and what still needs validation to earn regulatory and industry trust.

• Closing information gaps for novel modalities. Strategies to make AI useful when clinical and preclinical data for LNPs, cell therapies, and gene therapies are still limited.
• Combining AI + TSA + expert judgment. How preliminary target-associated literature insights and mechanistic toxicology guide model development where in vivo datasets don’t yet exist.
• From animal prediction to human relevance. Debating current limits of LLMs and ML tools in forecasting outcomes across species and modalities.
• Scaling confidence through validation & collaboration. What data standards, regulatory engagement, and partnerships between AI developers and toxicologists are needed for broader adoption.

• Understand how model performance is limited by the training dataset and why incomplete or narrow data subsets lead to reduced predictivity and continued reliance on in vivo studies
• Learn practical approaches from pharma using integrated in vitro and in silico data for screening while ensuring IND-enabling decisions remain grounded in translatable evidence
• Explore collaboration strategies between toxicologists and AI scientists to generate scalable datasets and build models that support future first-in-human decision making