The 2^nd workshop on Generative AI and Biology

Welcome to the GenBio Workshop! Join us as we explore the exciting intersection of artificial intelligence and biology. Discover how generative AI is revolutionizing protein research, RNA analysis, molecular design, drug discovery, and more. This workshop is designed to be interactive and practical, equipping you with the skills to utilize generative AI tools in your own biological research. Engage in stimulating discussions and collaborate with experts from diverse backgrounds. Together, let's unlock the potential of generative AI for biology.

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Overview

Over the past year, generative AI models have led to tremendous breakthroughs, from image and text generation, to protein folding and design. These recent successes illustrate the incredible potential of generative AI not only for digital applications, but also for basic science and healthcare. We are now able to predict protein structure from sequence alone; to characterize the function and interactions of biomolecules; to design such molecules never-before-seen in nature; and more. The impacts are profound: through generative AI, we can systematically understand and reprogram biology at an unprecedented level.

The goals of this workshop are to bridge the gap between the machine learning and biological communities; to connect leading researchers from both industry and academia; and to gain critical insights into the future of generative-AI-driven biology. We look forward to your participation in this exciting discourse on the future of biology and AI.

Call for Papers

We invite researchers, scientists, students, and industry professionals working in the domains of artificial intelligence, machine learning, computational biology, bioinformatics, and related areas to submit their original research or review papers. The scope of this workshop includes, but not limited to, the following topics.

Designing and optimizing novel and useful biomolecules

Rational protein design: Prediction and optimization of protein sequences and/or structures, incorporating constraints and prior knowledge
Small molecule drug design: Discovery and optimization of novel and effective small molecule therapeutics, incorporating information about the biological context
Next frontiers of de-novo design: Designing other biomolecules including peptides, oligonucleotides, antibodies, or targeted degraders

From first principles: generative modeling for biological data

Sequence-based methods: large language models for protein / genomic sequences, sequence-based molecular design
Graph-based methods: generative learning on biological graphs and networks, e.g., molecular graphs, protein-protein interaction networks, genome-wide association graphs
Geometric deep learning: generative modeling of biological structures as point clouds, surfaces, and other geometric objects

Open challenges in generative AI and biology (Special Track)

Large language models for scientific discovery: literature summarization, structured information extraction, identifying knowledge gaps and uncovering novel connections, formulation of scientific hypotheses
Finding common ground: systematic barriers, biological experiment design with GenerativeAI-in-the-loop
Identifying the right problems: pressing challenges in biology that are difficult to address via traditional means, gap between biological need and existing generative algorithms