Uploaded May 2026 | Updated September 2026, 2 weeks ago
Eric and Wendy Schmidt Center Symposium: Biomedical Science and AI
April 28 - 29, 2026
Day 2, Invited talk: Three Impactful problems in drug discovery that might be solvable with machine learning
Lindsay Edwards, Relation Therapeutics
Abstract:
Many companies claim to be ‘transforming drug discovery’ with AI. While this seems plausible for operational challenges (such as report writing) and medicinal chemistry, the fundamental problems of medicine discovery remain rooted in our imperfect understanding of human disease biology. At Relation we are tackling these problems head on. In this talk, I will discuss successes and failures, the challenges of excessive boosterism and signpost where we (as a company and as a collective) might be going… and what might prove to be useful avenues to explore. In the process I will give examples from our teams’ work, including DNA foundation models, modelling cellular perturbations and the need for custom approaches to reasoning in biology.
Speaker Bio:
Lindsay Edwards is an internationally recognised leader at the interface of machine learning, computational biology, and drug discovery. Across both academia and industry, he has consistently advanced the translation of science into medicine using computational sciences. In his academic career in Australia and at King’s College London, he developed innovative computational approaches to human metabolism, hypoxia, and respiratory physiology. His teams pioneered methods for modelling metabolic and transcriptomic data, applying these to diverse clinical contexts including respiratory disease, diabetes, hypertension, Parkinson's disease, and critical illness. In industry, Lindsay founded one of the first pharmaceutical data science divisions at GSK, embedding machine learning and data science into every stage of the discovery pipeline. As CTO of Relation Therapeutics, he has raised over $200M, creating one of the most significant TechBio companies rooted in scientific translation (including forging a landmark platform multi billion dollar collaboration with GSK). His role on the Scientific Advisory Board of the Schmidt Centre at the Broad Institute of Harvard and MIT further reflects his international collaborative reach, engaging with leading global scientists at the intersection of AI and life sciences. His publication record—over 40 papers in top biomedical journals (NEJM, JAMA, PNAS) and premier AI/ML venues (NeurIPS, ICML, ICLR)—is exceptionally rare, demonstrating dual impact across medicine and computational science.
For more information, visit broad.io/ewsc-symposium-2026
Copyright Broad Institute, 2026. All rights reserved.
Eric and Wendy Schmidt Center Symposium: Biomedical Science and AI
April 28 - 29, 2026
Day 2, Invited talk: Three Impactful problems in drug discovery that might be solvable with machine learning
Lindsay Edwards, Relation Therapeutics
Abstract:
Many companies claim to be ‘transforming drug discovery’ with AI. While this seems plausible for operational challenges (such as report writing) and medicinal chemistry, the fundamental problems of medicine discovery remain rooted in our imperfect understanding of human disease biology. At Relation we are tackling these problems head on. In this talk, I will discuss successes and failures, the challenges of excessive boosterism and signpost where we (as a company and as a collective) might be going… and what might prove to be useful avenues to explore. In the process I will give examples from our teams’ work, including DNA foundation models, modelling cellular perturbations and the need for custom approaches to reasoning in biology.
Speaker Bio:
Lindsay Edwards is an internationally recognised leader at the interface of machine learning, computational biology, and drug discovery. Across both academia and industry, he has consistently advanced the translation of science into medicine using computational sciences. In his academic career in Australia and at King’s College London, he developed innovative computational approaches to human metabolism, hypoxia, and respiratory physiology. His teams pioneered methods for modelling metabolic and transcriptomic data, applying these to diverse clinical contexts including respiratory disease, diabetes, hypertension, Parkinson's disease, and critical illness. In industry, Lindsay founded one of the first pharmaceutical data science divisions at GSK, embedding machine learning and data science into every stage of the discovery pipeline. As CTO of Relation Therapeutics, he has raised over $200M, creating one of the most significant TechBio companies rooted in scientific translation (including forging a landmark platform multi billion dollar collaboration with GSK). His role on the Scientific Advisory Board of the Schmidt Centre at the Broad Institute of Harvard and MIT further reflects his international collaborative reach, engaging with leading global scientists at the intersection of AI and life sciences. His publication record—over 40 papers in top biomedical journals (NEJM, JAMA, PNAS) and premier AI/ML venues (NeurIPS, ICML, ICLR)—is exceptionally rare, demonstrating dual impact across medicine and computational science.
For more information, visit broad.io/ewsc-symposium-2026
Copyright Broad Institute, 2026. All rights reserved.
