2025 Breakout Discussions
TUESDAY, NOVEMBER 11, 2025 | 17:20
IN-PERSON ONLY BREAKOUT:
Designing and Optimizing Molecular Glues and Proximity-based Drugs
Ian Churcher, MA, D.Phil., Founder & CEO, Janus Drug Discovery Consulting Ltd.
Ken Hsu, PhD, Stephen F. and Fay Evans Martin Endowed Associate Professor, Department of Chemistry, The University of Texas at Austin
Christopher Tame, PhD, Co-Founder & CEO, Ternary Therapeutics
- How to decide the best strategy, inhibition versus degradation, for pursuing targets?
- Discovery and validation of new chemistries and functionalities·
- Leveraging covalent chemistry, induced proximity to develop new degrader modalities
- Optimizing potency, selectivity, tissue specificity and PK properties
IN-PERSON ONLY BREAKOUT:
Novel Modalities, Ligases for Pursuing Challenging Drug Targets
Markus Queisser, PhD, Scientific Director, Protein Degradation, GSK
Roman Sarott, PhD, Research Group Leader, Max Planck Institute for Medical Research
Edward Tate, PhD, Professor, Chemical Biology, Imperial College London
- How to select the right target for degradation?
- How do we effectively apply protein degradation approaches to undruggable targets? ·
- Utilizing new assays and platforms for structural and mechanistic characterization·
- Finding new ligases and cellular pathways for inducing degradation
IN-PERSON ONLY BREAKOUT:
Strategies for Unlocking High Value Targets
Stefan Geschwindner, PhD, Director, Biophysics, AstraZeneca R&D Gothenburg
- Current and emerging biophysical approaches for hard-to-drug targets
- Direct to profiling strategies to speed drug discovery
- Novel biology approaches that help unlock high value targets
- Using small molecule RNA binders to tackle difficult drug targets
IN-PERSON ONLY BREAKOUT:
Lead Generation for Membrane Proteins
David Norton, PhD, Director, Medicinal Chemistry, Astex Pharmaceuticals Ltd.
Evan O'Brien, PhD, Assistant Professor, Biophysics & Biophysical Chemistry, The Johns Hopkins University School of Medicine
Chiara R. Valenzano, PhD, Senior Research Associate, Molecular Science, Astex Pharmaceuticals
- Biophysical approaches
- DNA-encoded libraries
- CryoEM
- Targeting ion channels
WEDNESDAY, november 12, 2025 | 16:30
IN-PERSON ONLY BREAKOUT:
Biophysical Tools for Targeting PPIs
Elisa Barile, PhD, Executive Director, Biophysics & Chemical Biology, Eli Lilly & Company
- Orthogonal biophysical and biochemical approaches: XRC, Cryo-EM, NMR, SPR/GCI, DSF, FRET, MS, flow induced dispersion analysis (FIDA)
- Testing funnels: which techniques to use, and where? (e.g., assessing lead potency vs. tracking PPI affinity shifts...)
- AI applications to PPI screening and drug design
IN-PERSON ONLY BREAKOUT:
Covalent Drug Discovery
Maurizio Pellecchia, PhD, Professor, Biomedical Sciences Division, University of California, Riverside
- Starting points for covalent drug discovery: covalent fragments v. reversible, potent binders?
- Targeting nucleophilic amino acids beyond cysteine
- Novel warheads: how to characterize and prioritize?
IN-PERSON ONLY BREAKOUT:
AI for Drug Design and Lead Optimization
Jose Carlos Gómez-Tamayo, Senior Scientist II, CADD, Johnson & Johnson Innovative Medicine
Petrina Kamya, PhD, Global Head of AI Platforms & Vice President, Insilico Medicine; President, Insilico Medicine Canada
Victor Sebastian Perez, PhD, Project Leader, Drug Discovery, SandboxAQ
Woody Sherman, PhD, CIO, Psivant Therapeutics
- Using generative chemistry to enhance physicochemical properties and ligand interactions
- Exploring diverse molecular structures and chemical space using LLMs and other tools
- Effective use of virtual screening and structure-activity predictions tools
- Using physics and computational chemistry to find novel targets and leads
IN-PERSON ONLY BREAKOUT:
AI/ML- True Impact in Drug Discovery Today
Michael Liebman, PhD, Managing Director, IPQ Analytics, LLC
Jordi Mestres, PhD, Founder & CSO, Chemotargets
Tudor Oprea, MD, PhD, CEO, Expert Systems, Inc.
- Conscious use of machine learning and deep learning models
- Quantity versus quality data in AI Managing expectation and frustration
- What can we expect from models?
- Biased data and biased models, local vs general models
- How good and generalizable is your model for AI/ML?
- Does your model cover an extensive and diverse chemical space?