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CS598-GBL - Gen Mod for BioMed & Life Sci

Course Console

Lectures: 0220 Siebel Center for Computer Science, MW: 9:30 AM – 10:45 AM

Member (NetID) Role Office Hours
Ge Liu (geliu) Instructor Mon 3:00-4:00 SC3212
Yanru Qu (yanruqu2) TA Mon 11-12 SC 3rd floor near elevator

Canvas: https://canvas.illinois.edu/courses/56424

  • For homework/report submission
  • For open discussion

Github (Course website): https://github.com/gelab-uiuc/CS598-GBL

  • Covering most of the course information, including schedule and reading lists

Note: Please use Canvas Discussion to submit your course-related questions. Please DO NOT email the TA or Prof. Liu directly, unless the matter is private or only concerns yourself.

Course Description

In this course, we will discuss recent advances in generative AI for biomedicine, with a special focus on geometric-aware deep learning, multimodal diffusion/flow matching on diverse data manifolds, and language models. We will also discuss foundational models for biomedicine and life sciences applications such as protein design, drug discovery, and understanding the functions and dynamics of complex biomolecular systems.

Learning Objectives: This course will introduce generative model basics, their application in biomedicines, and other impactful research works. At the end of this course, you will be able to:

  • Have a good overview and basic knowledge of state-of-the-art generative models in biomedicines & life science
  • Familiar with the research process: proposal, presentation, paper writing, review & rebuttal, hands-on experiments
  • Have critical thinking and assessment of research papers

Structure: This will be a graduate-level course in seminar format.

  • 6 instructor lectures: we will first review the biology & generative model basics. See schedule
  • 1 project brain-storm session
  • 15 paper-reading lectures: we will select 15 research sub-topics and reading lists for students to read, present, and discuss, covering the following topics:
    • Language models for biomedicine
    • Generative models for chemical molecules
    • Generative models for protein folding and design
    • Complex-based design with generative models: Structure-based drug design, peptide, antibody, enzyme, and molecular docking
    • RLHF/conditional generation/Guidance
  • mid-term and final presentations

Grading Policy

Groups: All activities of this course, except your attendance, will be performed in groups of 3 students, in a total of 15 groups. By Feb 3, most groups should submit their memberships (exactly 3) and topic preference (see 5 general topics in Structure). For students who do not find a group, we will assign groups according to their topic preference.

Component Weight Breakdown
Pre-class Reading 10% 10 pt 15 lectures
In-class Discussion 30% - 10% Presentation - 10% Rebuttal - 10% Review
Hands-on Experiments 20% - 10% Scripts & Generation Results - 10% Report
Final Project 40% - 5% Proposal - 15% Final Presentation - 20% Final Report

Pre-class Reading

In-class Discussion

  • In each paper-reading lecture, 2 groups will be signed up as the main leaders of the discussion, with each group playing one of the 2 roles: presenters or reviewers. These 2 groups are required to submit assignments (see below). Other groups are encouraged to participate in class discussions. Throughout the semester, each group needs to sign up for each one of the roles once, totaling participation in 2 different subtopics.
  • Group Assignment for designated presenters and reviewers:
    • For presenter: submit slides (at least 20 pages) for all required readings before the lecture, and present the papers during the class (45 min presentation); submit a rebuttal within 6 days after the lecture
    • For reviewers: submit academic reviews about required readings by EOD of the lecture

Hands-on Experiments

We’ll release 1-3 benchmark challenges during the semester. Each group should choose 1 challenge and use its codebase to generate results and evaluation metrics, and finish a short report like the experiment section in a paper. The report should cover experiment settings and results analysis. Each group should submit the experiment reports 2 weeks before the final presentation (Apr 23).

Final Project

Each group should complete a final project in one of the following types:

  1. Comprehensive literature survey on one advanced subtopic in generative AI for biomedicine (doesn't need to be one of the subtopics of the lecture, but should be related to the topics of the class).
  2. Benchmarking & dataset paper (opportunity to submit to Neurips Benchmark track)
  3. Open-ended research (opportunity to submit to Neurips main track)

Each group should submit a proposal by Mar 5 (1 week before mid-term), and a final report by May 14 (1 week after the final presentation)

Late Day Policy

Students may request one 3-day extension in the semester for full credit. Otherwise, late submission within 3 days results in at most 80% of credits. Submission later than 3 days results in 0% of credits.

Tentative Schedule

Note: This is an evolving list. For each topic, the presenter should cover 2-3 required papers in their presentation.

Date Topic Presenter Reviewer Note
Course Introduction
Jan 22 Intro + Biology101 Prof. Liu
Jan 27 Diffusion
Jan 29 Flow Models
Feb 3 VAE Group Membership & Topic Preference Due
Feb 5 Geometric DL & Equivariance
Feb 10 Seq Model & Discrete Generation
Feb 12 No Lecture / Brain storm for project proposal and prepare presentation
Paper-reading lectures begin
Language Model in Biomedicine
Feb 17 lecture 1: protein Kerui Chen, Wei Xia, Tao Feng Jinwei Yao, Yexin Wu, Haofei Yu
Feb 19 lecture 2: protein & beyond Emmanuel Buabeng, Diya Yunus, Kriti Mathur
Feb 24 lecture 3: discrete generation Enyi Jiang, Lily Xie, Huyen Nguyen
Molecule Generation
Feb 26 lecture 4: 2d Xiao Lin, Zhichen Zeng, Zihao Li
Mar 3 lecture 5: 3d Reihaneh Jahedan, Akash Arunabharathi, Ishaan Mathur Reihaneh Jahedan, Akash Arunabharathi, Ishaan Mathur
Mar 5 lecture 6: 2d & 3d Jingjie He, Chenhao Xu, Boyang Sun Proposal Due
Mid-term Presentation & Hands on challenge introduction
Mar 10
Mar 12
Spring Break
Mar 17 No Lecture
Mar 19 No Lecture
Paper-reading Lectures
Protein Generation
Mar 24 lecture 7: folding and inverse folding Jinwei Yao, Yexin Wu, Haofei Yu
Mar 26 lecture 8: folding and inverse folding Hanyang Chen, Hangke Sui, Hesun Chen Ziwen Wang, Maohong Liao, Jiajun Fan
Mar 31 lecture 9: folding and inverse folding
Apr 2 lecture 10: co-design
Complex-based Generation
Apr 7 lecture 11: peptide design
Apr 9 lecture 12: structure-based drug design John Wu, Siddhartha Laghuverapu, Jathurshan Pradeepkumar
Apr 14 lecture 13: docking
Apr 16 lecture 14: antibody design
RLHF/DPO/Guidance
Apr 21 lecture 15 Ziwen Wang, Maohong Liao, Jiajun Fan
Apr 23 Remote, DLL for challenge submission
Apr 28 No Lecture / Work on Final Presentation
Apr 30 No Lecture / Work on Final Presentation
Final Presentation
May 5
May 7
May 14 Final Report due

Reading List

Full Reading List

Guidelines

Presentation & Slides

  • 40-45 min presentation, at least 20 pages
  • cover the content of required papers (paper above optional)
  • be ready to answer questions
  • you are encouraged to read some of those optional readings but not required

Pre-class Questions

  • every group should submit at least 1 question or idea before class

Review

Review template (reference from ICLR 2025):

  • Summary [Provide a brief summary of the paper, including the main problem being addressed, key contributions, and proposed methods.]

  • Evaluation Scores (1: poor, 2: weak, 3: fair, 4: good, 5: excellent) Soundness: [Score: 1-5] (Does the paper provide a sound and well-supported argument?) Presentation: [Score: 1-5] (Is the paper clearly written and well-structured?) Contribution: [Score: 1-5] (Does the paper make a meaningful contribution to the field?)

  • Strengths [List the key strengths of the paper. Be specific about the aspects that are well-executed, such as novelty, clarity, experimental design, or real-world applicability.]

  • Weaknesses [Identify limitations or areas where the paper could be improved. Be constructive and suggest ways to address the issues.]

  • Questions [Pose specific questions for clarification and provide constructive suggestions for improvement.]

  • Ethical Considerations Ethics review needed No ethics review needed [Comment on potential ethical concerns, such as bias, data privacy, or misuse of the method.]

  • Final Recommendation

    • Rating: [Score: 1-10] (How strong is this paper in its current form?)
    • Confidence Level: [Score: 1-5] (How confident are you in your assessment?)
rating meaning
10 Strong accept (Top 5% of ICLR submissions, a must-accept)
9 Clear accept (Excellent work, high confidence in acceptance)
8 Accept (Solid contribution, well-executed research)
7 Weak accept (Good paper but with some minor issues)
6 Marginally above the acceptance threshold (Could be accepted, but not strong)
5 Marginally below the acceptance threshold (Has merit but concerns remain)
4 Weak reject (Not strong enough for ICLR, requires major improvements)
3 Reject (Significant issues, not suitable for ICLR in its current form)
2 Strong reject (Serious flaws, major revisions needed before resubmission)
1 Trivial/invalid submission (Should not have been submitted in its current state)
confidence level meaning
5 Very confident – The reviewer is an expert in the topic and fully understands the paper. It is highly unlikely that they missed something important.
4 Confident – The reviewer is familiar with the topic and reasonably certain about their assessment. There is a small chance they missed something.
3 Moderately confident – The reviewer understands the general ideas but is not deeply familiar with all technical details. Some aspects may require further verification.
2 Limited confidence – The reviewer is not very familiar with the topic and may have misunderstood some aspects. Additional expert opinions are needed.
1 Not confident – The reviewer does not feel qualified to assess the paper. They are unsure if their judgment is correct and strongly recommend additional expert input.

Rebuttal

The rebuttal phase provides authors with an opportunity to clarify misunderstandings, address reviewer concerns, and strengthen their paper’s positioning. Below are key guidelines to ensure an effective response:

  • Address Key Concerns First Focus on the most critical points raised by the reviewers, such as concerns about methodology, experimental validity, and novelty. Prioritize addressing low scores in soundness, contribution, or presentation, as these impact acceptance chances the most.

  • Be Clear, Concise, and Professional Stick to the facts: Provide clear, well-structured responses rather than lengthy justifications. Maintain a professional tone: Avoid defensive language. Acknowledge valid points and explain improvements or clarifications.

  • Provide Additional Evidence (If Possible) If a reviewer questions experimental validity, clarify the setup and assumptions. If applicable, include new results or brief additional analysis (e.g., ablation studies, comparisons with missing baselines). If a misunderstanding occurred, restate your approach clearly and provide citations if necessary.

  • Organize Responses for Readability Summarize key points before responding to each review. Use clear formatting, such as bullet points or bolded keywords, to make the response easy to follow. Address each reviewer separately, referencing their comments explicitly (e.g., Reviewer 2 questioned the experimental setup...).

  • Do Not Introduce Major Changes The rebuttal is for clarifications and justifications, not for significant changes to the method or experiments. If major revisions are needed, acknowledge them and indicate how they will be addressed in a future version.

Experiment Report

todo

Final Report

todo

Acknowledgements

In course structure design, this course is heavily inspired by other seminar-like courses, particularly UIUC CS598-GenAI System. Acknowledgments to Prof. Fan Lai for generous sharing of his great course.

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