Workshop B – Advancing Predictive Modeling for Affinity, Binding Poses & Protein Design to Improve In Silico Decision Making & Candidate Selection
1:00 pm - Tuesday 2nd February 2027
Accurately predicting how molecules bind to proteins remains one of the major unsolved challenges in structure-based drug design, with current approaches struggling to rank binding affinities, predict accurate poses and design proteins with sufficiently high success rates. This technical workshop will explore how AI, physics-based modeling and experimental validation can be combined to improve predictive performance by:
- Improving small-molecule docking and binding pose prediction by examining the limitations of current approaches, including challenges with homologous versus novel structures and the difficulty of accurately modeling protein-ligand interactions
- Advancing affinity prediction and free-energy calculations to better rank compounds by exploring how AI and physics-based approaches can address complex parameters such as protein flexibility, solvent and water interactions that limit current predictive accuracy
- Strengthening de novo protein design and computationally designed binders by examining how prediction models can be improved, experimentally validated and coupled with high-throughput screening to increase the success rate of designed proteins
- Benchmarking predictive models against experimental data to identify which AI/ML approaches genuinely improve SBDD outcomes and where expert oversight and wet-lab validation remain essential
