Generative and Experimental Perspectives for 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.

The Generative and Experimental perspectives in bioMolecular design (GEM) workshop seeks to bridge this gap by bringing computationalists and experimentalists together. Together, we will explore the strengths and challenges of generative ML in biology, experimental integration of generative ML, and pinpoint biological problems ready for ML.

GEM is collaborating with Cell Systems to allow exceptional submissions to be considered for fast-tracking in their journal. GEM features an in-silico generative machine learning track as well as an experimental track for papers that have wet lab results.

Our lineup features renowned scientists as speakers and emerging leaders 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.

GEM is an in-person workshop on May 11th, in Vienna, Austria, at ICLR’24. We would be thrilled to have your participation at the workshop, including submitting a paper or signing up as a reviewer. Kindly share this information with anyone who might be interested. For any inquiries, feel free to reach out to us at gembioworkshop@googlegroups.com or connect with us on social media. We look forward to your participation!