Integrating Generative and Experimental Platforms for Biomolecular Design

Cheng-Hao Liu, Jarrid Rector-Brooks, Soojung Yang, Sidney L Lisanza, Francesca-Zhoufan Li, Hannes Stark, Jacob Gershon, Lauren Hong, Pranam Chatterjee, Tommi Jaakkola, Regina Barzilay, David Baker, Frances H. Arnold, Yoshua Bengio

Keywords: Generative machine learning, Biomolecular design

Abstract: Biomolecular design, through artificial engineering of proteins, ligands, and nucleic acids, holds immense promise in addressing pressing medical, industrial, and environmental challenges. While generative machine learning has shown significant potential in this area, a palpable 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. With a diverse organizing team and backed by industry sponsors, we dedicate the workshop to pushing the boundaries of ML's role in biology.

Submission Number: 65