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
Edward Tate, PhD, Professor, Chemical Biology, Imperial College London
- 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
Stefan Knapp, PhD, CSO, Structural Genomics Consortium, Johann Wolfgang Goethe-University, Frankfurt
Markus Queisser, PhD, Scientific Director, Protein Degradation, GSK
Roman Sarott, PhD, Research Group Leader, Max Planck Institute for Medical Research
- 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?