Deep Learning for Drug Discovery

Organizers

Prof. Dr. Verena Wolf, Gerrit Großmann, Michael Backenköhler.

Prof. Dr. Olga Kalinina, Ilya Senatorov (ilya.senatorov@helmholtz-hips.de), Roman Joeres (roman.joeres@helmholtz-hips.de).

This is a joint seminar with the Deep Learning for Drug Discovery Seminar by Prof. Olga Kalinina’s group.

Please contact Gerrit Großmann for questions and have [DLDDSeminar] in the subject line.

Registration

Please use the seminar assignment system to register. Bioinformatics students should apply for Prof. Kalinina’s seminar.

Topic

The discovery of novel therapeutic drugs is extremely costly and time-consuming. In silico drug discovery can potentially accelerate this process. However, this method requires exploring the vast chemical space to find molecules of interest. High expectations are placed on deep learning methods to simplify this process and provide navigation in the realm of possible molecules.

This seminar focuses on deep learning methods for learning molecular representations, predicting molecular properties and, ultimately, for finding novel molecules with desired properties. Primarily, we examine recent advances in the field of geometric deep learning and graph neural networks.

Organisation

• This seminar takes place Thursdays 10:15 to 11:45.
• A kick-off meeting will be held on Thursday, April 21, 2022.
• The first session will be held two weeks later (Thursday, May 5, 2022).
• The seminar will take place in-person in E2.1 (ZBI) Room 001.
• The seminar language is English.
• The seminar earns you 7 ECTS.
• The seminar is eligible for bachelor and master students of computer science and related courses.
• The seminar includes 10 sessions (with 2 presentations each).

Requirements

Prior knowledge of biochemistry is not expected. General background knowledge in deep learning is recommended.

Grade

To pass, you have to attend all sessions (in person), give a presentation, participate in discussions, and submit reports.

The final grade consists of: your presentation (50%), reports (30%), and participation during the seminar discussions (20%).

Presentation (50%)

Identify the key ideas and concepts and give a self-consistent presentation explaining these concepts to your fellow students. The presentation should focus on intuition and high-level understanding and contextualization, not on technical details. We encourage the students to read related work and explore supplementary material and include it in the presentation where it seems useful (e.g., from related literature, YouTube, GitHub, Medium articles, OpenReview, etc.).

Send a first draft of your slides as a .pdf file to your supervisor 7 days before the presentation (deadline is Thursdays 23:59).

The presentation should be 25 minutes long.

Suggestions for a good presentation:

• The goal is to tell a self-consistent and entertaining story — not to convince us that you understand the paper.
• Put time and effort into creating visualizations and working out (running) examples.
• Prioritize concreteness, simplicity, and clarity.
• Don’t overcrowd your slides. Avoid full sentences and be cautious with bullet points.
• Use equations only when necessary; use color-coded equations to improve their readability.
• Be critical of the authors’ claims, don’t fall for overselling.

Reports (30%)

Every participant has to write a short report for each paper in their group (except their own). Each report should be about two to three pages long. The report should contain a short summary of (what you consider to be) the main contribution or most intriguing idea of the paper. Otherwise, you can freely express your own thoughts on the topic. For instance: What did you like/dislike about the paper (both methodically and didactically)? What are connections to other seminar papers? Can you suggest improvements? What do you think is missing?

The report deadline is 14 days after the last presentation (August 4, 23:59).

You can use the NeurIPS or Springer LNCS LaTeX template or any other reasonable format (don’t write an abstract). Please use a spell+grammar checker like LanguageTool or Grammarly before submitting.

Discussions (20%)

Read each presented paper in advance (of course, we do not expect you to understand them fully) and actively participate in the discussion. We expect each participant to attend each session.

Content

Compulsory Reading List

• Geometric Deep Learning on Molecular Representations
• Towards the sustainable discovery and development of new antibiotics

Recommended Reading/Watch List

• Video: Theoretical Foundations of Graph Neural Networks
• Video: ICLR 2021 Keynote - “Geometric Deep Learning: The Erlangen Programme of ML” - M Bronstein
• Video: Can graph neural networks understand chemistry? - Dominique Beaini

Background Knowledge

• Deep Learning on Graphs - Yao Ma and Jiliang Tang
• Graph Representation Learning Book - William L. Hamilton

Paper List and Time Table

Type indicates whether a paper is suggested for bioinformatics (B) or CS (CS) students.

Image credit: Tara Winstead