We welcome extended abstracts (2-4 pages, non-archival), including works-in-progress and preliminary results. Topics of interest include but are not limited to:

• Prediction of biomolecular structures, properties, complexes, and interactions
• Generative models for biological sequences and structures
• Bayesian optimization and active learning for guided biological experimentation
• Geometric and symmetry-aware deep learning for molecular representations
• Interpretable and uncertainty-aware models for biological design
• Multi-modal learning over sequence, structure, and functional data
• Benchmarks, datasets, and evaluation protocols for biological design tasks
• Foundation models for biology
• Novel applications of AI in computational and experimental biology
• Position papers and/or posters addressing fundamental challenges or limitations of AI in computational and experimental biology