09:00 – 9:10 Opening Remarks
09:10 -10:30 | Session I: De-novo Drug Design
- 09:10 – 09:40 Gisbert Schneider (ETH Zürich) Drug design with machine intelligence
- 09:40 – 10:10 Djork-Arné Clevert (Pfizer) From Models to Molecules: Controllable, Structure-Based Molecular Generation
- 10:10 – 10:20 Mahule Roy, Subhas Roy: Co-Diffuse: Generative Co-Design of Protein–Ligand Interactions via 3D Equivariant Diffusion Models with Induced-Fit Awareness
- 10:20 – 10:30 Alessandro Luongo, Konstantina Koteva, El Amine Cherrat, Enrico Petretto, Alexander Rodin Quantum-Enhanced Generative AI Pipeline for Molecular Design
10:30 – 11:00 Coffee/Tea Break
11:00 – 12:00 | Session II: Active Learning
- 11:00 – 11:30 J.B. Brown (Boehringer-Ingelheim) Active Learning Drug Discovery at Boehringer-Ingelheim : expectations, realities, and best practices
- 11:30 – 12:00 Jake Taylor-King (Relation Therapeutics) AI in genomics: genetics, spatial transcriptomics, and experimental design
- 12:00 – 12:10 Samarth Kadaba, Connor C. Call, Alexander E. Vlahos, Hank Jones, Xiaojing J Gao SMPO: Score-Matching Preference Optimization for Protein Fitness Modeling
- 12:10 – 12:20 Mehyar Mlaweh, Tristan Cazenave, Ines Alaya BeeRNA: tertiary structure-based RNA inverse folding using Artificial Bee Colony
12:20 – 13:50 Lunch
13:50 – 15:10 | Session III: Multi-Modal Learning:
- 13:50 – 14:20 Le Song (GenBio AI) Towards AI-Driven Digital Organism: A System of Multiscale Foundation Models for Biology
- 14:20 – 14:50 Jianying Hu(IBM) GenAI for Biomedical Research and Discovery
- 14:50 – 15:00 Le Huy Khiem, Sreejata Dey, Marcos Martínez Galindo, Vanessa Lopez, Ting Hua, Nitesh V Chawla, Hoang Thanh Lam Towards Generalist Large Language Models for Molecular Property Prediction: Distilling Knowledge from Specialist Models
- 15:00 – 15:10 Ahmed A. A. Elhag, Arun Raja, Alex Morehead, Samuel M Blau, Garrett M Morris, Michael M. Bronstein Learning Inter-Atomic Potentials without Explicit Equivariance
15:10 – 16:30 Poster Session & Coffee Break
16:30 – 17:50 | Session IV: Causal AI
- 16:30 – 17:00 Patrick Schwab (GSK) Working smarter, not harder in drug development: AI assistants for accelerating biological discovery
- 17:00 – 17:30 Yoshihiro Yamanishi (Nagoya University) Data-driven identification of therapeutic targets and drug candidates
through machine learning - 17:30 – 17:40 David Scott Lewis, Enrique Zueco Agentic Causal Graph Learning for Drug Target Discovery: A Self-Directed AI System at STRING Scale
- 17:40 – 17:50 Crystal Su MedRule-KG: A Knowledge-Graph–Steered Scaffold for Reliable Mathematical and Biomedical Reasoning
17:50 – 18:00 Closing Remarks