Comparing Selectivity-Aware Generative AI and Library Screening in a Virtual DMT Cycle

Biomolecular design, through artificial engineering of proteins, ligands, nucleic acids, and cells, holds immense promise in addressing pressing medical, industrial, and environmental challenges. While generative machine learning has shown significant potential in this area, a disconnect exists with experimental biology: many ML research efforts prioritize static benchmark performance, potentially sidelining impactful biological applications. This workshop seeks to bridge this gap by bringing computationalists and experimentalists together, catalyzing a deeper interdisciplinary discourse. Together, we will explore the strengths and challenges of generative ML in biology, experimental integration of generative ML, and biological problems ready for ML. To attract high-quality and diverse research, we partnered with Nature Biotechnology for a special collection, and we created dedicated tracks for in-silico ML research and hybrid ML-experimental biology research. Our lineup features emerging leaders as speakers and renowned scientists as panelists, encapsulating a spectrum from high-throughput experimentation and computational biology to generative ML. To catalyze new collaborations, we will host a seed-grant competition for pairs of experimentalists and computationalists proposing fresh joint projects. To connect dry and wet lab practice, a wet-lab challenge sponsored by Adaptyv Bio will empirically evaluate protein design models. With a diverse organizing team and backed by industry sponsors, we dedicate the workshop to pushing the boundaries of ML's role in biology. This will be the third edition of this workshop following the previous versions of it we organized at ICLR 2024 and 2025.