The 1st 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

The revolutionary crossroads between artificial intelligence (AI) and biology is one of the most exciting frontiers of our time. In this workshop, we will dive deeply into the implications of generative AI for biological discovery, drug discovery, and translational medicine.

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

Submission Instructions

We are inviting experienced researchers to participate in the review process as program committee members. If you are interested, please submit your request here!

You are invited to submit your papers in our OpenReview submission portal. All submissions must be anonymous for double-blind review. We expect each paper to be reviewed by at least three reviewers. The content of the paper (excluding the references and supplementary materials) should not be longer than 4 pages, with strict adherence to the NeurIPS template style, which can be found here.

Authors may submit up to 100 MB of supplementary materials separately. Authors are highly encouraged to submit their code for reproducibility.

According to the NeurIPS workshop guidelines, we do not encourage the re-submission of already-published papers, but you are allowed to submit ArXiv pre-prints or those currently under submission. Moreover, a work that is presented at the NeurIPS main conference should not appear in a workshop. Please be sure to indicate conflicts of interest for all authors on your paper.

To encourage higher quality submissions, we will offer Best Paper Award(s) based on nomination by the reviewers and extensive discussions among the chairs. Furthermore, outstanding submissions will also be selected for oral or spotlight presentations. Bear in mind that our workshop is not archival, but accepted papers will be hosted on the workshop website.

(Tentative) Important Dates:

All deadlines are 11:59 pm UTC -12h ("Anywhere on Earth").

  • Submission Deadline (all authors must have an OpenReview profile when submitting): September 29, 2023
  • Acceptance Notification: October 20, 2023
  • Camera-Ready Submission: November 15, 2023
  • Workshop Date: Saturday, December 16, 2023 (in-person)

Confirmed Speakers & Panelists

David Baker
Professor
David Baker

U of Washington
Anima Anandkumar
Professor
Anima Anandkumar

Caltech, NVIDIA
Max Welling
Professor
Max Welling

U of Amsterdam, MSR
Debora Marks
Professor
Debora Marks

HMS, Broad
Ron Dror
Associate Professor
Ron Dror

Stanford
Daphne Koller
CEO
Daphne Koller

Insitro
Kyunghyun Cho
Associate Professor
Kyunghyun Cho

NYU, Genentech
Smita Krishnaswamy
Associate Professor
Smita Krishnaswamy

Yale
Jian Tang
Associate Professor
Jian Tang

Mila
Ellen Zhong
Assistant Professor
Ellen Zhong

Princeton
Connor Coley
Assistant Professor
Connor Coley

MIT
Shuiwang Ji
Professor
Shuiwang Ji

Texas A&M

Schedule (New Orleans Time Zone)

TBD

Organizers

Minkai Xu
Minkai Xu
Stanford
Wenxian Shi
Wenxian Shi
MIT
Rachel Wu
Rachel Wu
MIT
Zhenqiao Song
Zhenqiao Song
UCSB
Regina Barzilay
Professor
Regina Barzilay

MIT
Stefano Ermon
Professor
Stefano Ermon

Stanford
Jure Leskovec
Professor
Jure Leskovec

Stanford
Fan Yang
Dr.
Fan Yang

Tencent
Lei Li
Professor
Lei Li

CMU



Technical Committee

TBD



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