AI and ML: The Future of Target and Drug Discovery

At the 2026 Target ALS Annual Meeting, moderator Puneet Batra led a forward-looking roundtable with leaders from biopharma, AI-driven biotech, and venture capital to explore how AI and machine learning are already influencing target discovery in neuroscience — and where the field is headed next.

Aj Kaykas, Chief Exploration Officer and Head of Neuroscience at insitro, makes the case that Target ALS’s high-quality, consistently formatted data sets represent a unique opportunity for machine learning (ML) to transform ALS research. Having previously worked at the Allen Institute and Novartis on data-intensive biology projects, Kaykas explains how researchers can now “slice across” multi-omics data and patient trajectories to identify meaningful patterns.

Annalisa Pawlosky, Senior Staff Research Scientist at Google, introduces an innovative application of artificial intelligence to ALS research. Her team’s “agentic model” system, Co-Scientist, functions as a novel hypothesis generator by synthesizing scientific literature to surface unique mechanisms and connections, backed by web search and evidence-based reasoning. Hear how AI is being positioned not to replace scientists, but to help them ask better questions and find connections across the rapidly growing body of ALS research.