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 We will use Slack for open discussions. An invitation is sent to registered students.

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

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

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

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 brainstorming session
• 13 paper-reading lectures: we will select 13 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

• Group Assignment: Each paper-reading lecture will have several required readings and optional related readings. Each group should read at least one of the papers and submit at least 1 insightful question or idea related to the paper you read here https://docs.google.com/spreadsheets/d/1Q-wPi2Ezbv0QJe8hN2tBNC15WmkPaquhuJqkT4p5bWk/edit?usp=sharing before the lecture.

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 2 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	Prof. Liu
Jan 29	Flow Models	Prof. Liu
Feb 3	VAE	Prof. Liu		Group Membership & Topic Preference Due
Feb 5	Geometric DL & Equivariance	Prof. Liu
Feb 10	Seq Model & Discrete Generation	Prof. Liu
Feb 12	No Lecture / Brain storm for project proposal and prepare presentation
	Language Model in Biomedicine
Feb 17	lecture 1: protein	G2	G1
Feb 19	lecture 2: protein & beyond	G6	G3
Feb 24	lecture 3: discrete generation	G10	G2
Feb 26	Discussion for L1-L3
	Molecule Generation
Mar 3	lecture 4: 2d	G3	G11
Mar 5	lecture 5: 3d	G11	G10
Mar 10	lecture 6: 2d & 3d	G7	G6
Mar 12	Discussion on Small molecule			Proposal Due mar 16
	Spring Break
Mar 17	No Lecture
Mar 19	No Lecture
Mar 24	Mid term presentation
Mar 26	Protein design primer & Hands on challenge overview	Prof. Liu
	Protein
Mar 31	Guest Lecture on MSA			by Prof. Tandy Warnow
Apr 2	lecture 7: folding inverse folding	G1	G4
Apr 7	lecture 8: structure gen	G5	G8
Apr 9	lecture 9: structure gen & co-design	G9	G5
Apr 14	Discussion on Protein design
	Complex-based Generation
Apr 16	lecture 10: peptide and antibody design
Apr 21	lecture 12: structure-based drug design	G4	G7	TA host, Instructor remote
	RLHF/DPO/Guidance
Apr 23	lecture 13	G8	G9	TA host, Instructor 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

See requirements in Canvas

Final Report

See requirements in Canvas

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.