Richard Bonneau, Vice President of Machine Learning for Drug Discovery at Genentech and Roche, provides Pitt’s HexAI podcast host, Jordan Gass-Pooré, with an insider view on how his team is fundamentally changing and accelerating how new drug candidate molecules are designed, predicted, and optimized.

Geared for students in computational sciences and hybrid STEM fields, the episode introduces listeners to uses of AI and ML in molecular design, the biomolecular structure and structure-function relationships that underpin drug discovery, and how distinct teams at Genentech work together through an integrated computational system.

Richard and Jordan use the opportunity to touch on how advances in the molecule design domain can inspire and inform advances in computational pathology and laboratory medicine. Richard also delves into the critical role of Explainable AI (XAI), interpretability, and error estimation in the drug design-prototype-test cycle, and provides advice on domain knowledge and skills needed today by students interested in joining teams like his at Genentech and Roche.

Dennis Wei, Senior Research Scientist at IBM specializing in human-centered trustworthy AI, speaks with Pitt’s HexAI podcast host, Jordan Gass-Pooré, about his work focusing on trustworthy machine learning, including interpretability of machine learning models, algorithmic fairness, robustness, causal inference and graphical models.

Concentrating on explainable AI, they speak in depth about the explainability of Large Language Models (LLMs), the field of in-context explainability and IBM’s new In-Context Explainability 360 (ICX360) toolkit. They explore research project ideas for students and touch on the personalization of explainability outputs for different users and on leveraging explainability to help guide and optimize LLM reasoning. They also discuss IBM’s interest in collaborating with university labs around explainable AI in healthcare and on related work at IBM looking at the steerability of LLMs and combining explainability and steerability to evaluate model modifications.

This episode provides a deep dive into explainable AI, exploring how the field's cutting-edge research is contributing to more trustworthy applications of AI in healthcare. The discussion also highlights emerging research directions ideal for stimulating new academic projects and university-industry collaborations.

Guest profile: https://research.ibm.com/people/dennis-wei

ICX360 Toolkit: https://github.com/IBM/ICX360

Jason Moore, Chair of the Department of Computational Biomedicine and Director of the Center for Artificial Intelligence Research and Education (CAIRE) at Cedars-Sinai Medical Center in Los Angeles, CA, speaks with Pitt’s HexAI podcast host, Jordan Gass-Pooré, about his work, the strategic investments his center is making in technology and specialized human expertise to support advanced AI research and about the incorporation of AI and AI agents into precision health.

They speak in depth about the recent and rapid emergence of agentic AI, which is expected to have a significant impact on healthcare and how his team’s work is advancing the field. They also touch on vetting, deploying, and monitoring AI models for clinical use; explainable AI, trust, and transparency; using AI chatbots to improve the patient experience; the importance of building effective collaborations between industry and academia; and Cedar-Sinai’s new PhD program in Health AI.