Graph Neural Networks | Neuro-Mechanistic Modeling

Evaluation beyond goodness of fit: Quantifying biophysical alignment of AI models for kinase-centric drug discovery

Mon, 01 Jun 2026 00:00:00 +0000

Hands-On Graph Neural Networks

Tue, 01 Oct 2024 00:00:00 +0000

Lecturers

Joschka Groß, M.Sc.
Felix Jahn, M.Sc.
Prof. Dr. Verena Wolf

The Hands-on GNNs seminar covered the basics of graph neural networks and their applications in various domains. The seminar was held in the winter semester 2024. Participants read and presented papers on the topic and summarized their learnings in easy-to-follow-along talktorials. These are Jupyter notebooks that guide the reader through select topics concerning the fundamentals of GNNs and more advanced topics.

Organization

For all organizational matters, please first consult the CMS or contact Joschka Groß.

Final talktorials

Coming soon.

Neuro-symbolic General Policy Learning

Tue, 01 Oct 2024 00:00:00 +0000

Supervisors

Timo P. Gros, Jörg Hoffmann, Daniel Höller, Nicola J. Müller and Prof. Dr. Verena Wolf

Content

Symbolic AI relies on explicit, human-readable symbols and rules to represent knowledge and solve problems. This approach contrasts with modern, data-driven methods like machine learning, which rely on statistical patterns rather than explicit rules. Recently, both approaches have been combined, leading to the development of neuro-symbolic methods.

For instance, heuristic search, as a very successful part of symbolic AI, must be applied individually to each instance of a domain or problem. In contrast, neuro-symbolic general policy learning focuses on neural network-based policies that can solve all instances within a specific domain, combining the benefits of symbolic and data-driven AI.

In this seminar, we explored the emerging trend of learning neuro-symbolic general policies where the underlying architecture is based on Graph Neural Networks or Transformers.

Format

A seminar with 7 graded ECTS points, run in block format: an initial meeting in November and a block of student presentations in March. Each participant read and understood a piece of research, wrote a summary paper in their own words, gave a presentation, and gave detailed feedback on the paper and presentation of a fellow student.

Topics

Area 1: Analysis of General Policies (supervisor: Timo P. Gros)

Simon Ståhlberg, Blai Bonet, and Hector Geffner. Learning general optimal policies with graph neural networks: Expressive power, transparency, and limits.
Simon Ståhlberg, Blai Bonet, and Hector Geffner. Learning generalized policies without supervision using GNNs.
Rostislav Horčík and Gustav Šír. Expressiveness of graph neural networks in planning domains.
Dillon Z. Chen, Sylvie Thiébaux, and Felipe Trevizan. Learning domain independent heuristics for grounded and lifted planning.

Area 2: Training of General Policies (supervisor: Daniel Höller)

Sam Toyer, Felipe Trevizan, Sylvie Thiébaux, and Lexing Xie. Action schema networks: Generalised policies with deep learning.
Or Rivlin, Tamir Hazan, and Erez Karpas. Generalized planning with deep reinforcement learning.
Simon Ståhlberg, Blai Bonet, and Hector Geffner. Learning general policies with policy gradient methods.

Area 3: General Policies using Transformers (supervisor: Nicola J. Müller)

Nicola J. Müller, Pablo Sánchez, Jörg Hoffmann, Verena Wolf, and Timo P. Gros. Comparing state-of-the-art graph neural networks and transformers for general policy learning.
Nicholas Rossetti, Massimiliano Tummolo, Alfonso Emilio Gerevini, Luca Putelli, Ivan Serina, Mattia Chiari, and Matteo Olivato. Learning general policies for planning through GPT models.
Lucas Lehnert, Sainbayar Sukhbaatar, Paul Mcvay, Michael Rabbat, and Yuandong Tian. Beyond A*: Better planning with transformers via search dynamics bootstrapping.
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch. Decision transformer: Reinforcement learning via sequence modeling.

Organization

All information and materials are available in the CMS.

Deep Learning for Drug Discovery

Thu, 21 Apr 2022 00:00:00 +0000

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

Background Knowledge

Paper List and Time Table

Date	Type	Student (Advisor)	Topic	Group
05.05.2022	B	Roshni Biswas (Roman)	Convolutional Networks on Graphs for Learning Molecular Fingerprints	D
05.05.2022	CS	Advait Ganesh Athreya (GG)	Neural Message Passing for Quantum Chemistry	D
12.05.2022	B	Mohammadmahdi Koochali (Roman & Ilya)	SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient	A
12.05.2022	B	Pegah Einaliyan (Roman & Ilya)	Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models	A
19.05.2022	CS (MB)	Shreyash Arya	Junction Tree Variational Autoencoder for Molecular Graph Generation	A
19.05.2022	CS (GG)	Ashwath Pravin Shetty	MolGAN: An implicit generative model for small molecular graphs	A
26.05.2022	–	–	No session – Christi Himmelfahrt	–
02.06.2022	B	Nancy Singh (Roman)	Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation	B
02.06.2022	CS (GG)	~~August von Liechtenstein~~	~~Learning to Extend Molecular Scaffolds with Structural Motifs~~ (presentation did not take place, write a report anyway)	C
09.06.2022	B	Joel Chavarria Rivera (Ilya)	Model agnostic generation of counterfactual explanations for molecules	B
16.06.2022	–	–	No session – Fronleichnam	–
23.06.2022	CS (MB)	Martin Bálint	Weisfeiler and Lehman Go Cellular: CW Networks	E
23.06.2022	CS (MB)	Kartik Teotia	E(n) Equivariant Graph Neural Networks	E
30.06.2022	B	Gilbert El Khouri (Ilya)	Molecular machine learning with conformer ensembles	C
30.06.2022	B	Aleksandra Kushnareva (Ilya)	GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles	C
07.07.2022	CS	Ibrahim Siddig Ibrahim Eltayeb (MB)	SchNet: A continuous-filter convolutional neural network for modeling quantum interactions	D
07.07.2022	CS	Irem Begüm Gündüz (GG)	Spherical Message Passing for 3D Graph Networks	C
14.07.2022	B	Tanya Malkani (Roman)	Self-supervised Graph Transformer on Large-Scale Molecular Data	E
14.07.2022	CS	Kinaan Aamir Khan (MB)	Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction	E
14.07.2022	B	Sneha Thomas (Roman)	Spec2Mol: An end-to-end deep learning framework for translating MS/MS Spectra to de-novo molecules (originally planned on 9.6.)	B
21.07.2022	B	Nazligul Keske (Ilya)	Drug-target affinity prediction using graph neural network and contact maps	D
21.07.2022	CS	Mattes Alexander Warning (GG)	Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design	B

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

Image credit: Tara Winstead

Exploring Graph Neural Networks

Mon, 19 Apr 2021 00:00:00 +0000

Organisation: Prof. Dr. Verena Wolf, Gerrit Großmann.

Please contact Gerrit Großmann for questions about the seminar and have [GNNSeminar] in the subject line.

Registration

Please use the seminar assignment system to register.

Topic

Graphs and networks are ubiquitous in nature, and graph-structured data naturally occurs in a variety of disciplines including physics, chemistry, neuroscience, biology, social science, and epidemiology.

This seminar addresses the problem of learning from graph-structured data. Traditional machine learning and deep learning techniques are not well suited to process data containing rich relational information. Graph Neural Networks (GNNs), Graph Machine Learning, and Geometric Deep Learning offer an interesting perspective on this challenge and are currently one of the fastest-growing topics in ML research. GNNs can assist in discovering drugs, fighting cancer, detecting neurological disorders, analyzing complex systems, recommending friendships in online social networks, controlling traffic, as well as studying epidemiological processes.

We examine recent developments in graph ML research, study state-of-the-art techniques, discuss their limitations, and explore various applications.

A neat overview of GNNs is given in the Graph Neural Networks with Petar Veličković talk.

Organisation

This seminar is a block seminar and will be given on September 23 and 24, 2021, starting at 10:00 sharp.
An online kick-off meeting will be held on Monday, April 19, at 2 pm (1 hour max).
The seminar language is English.
The seminar earns you 7 ECTS.
The seminar is eligible for bachelor and master students of computer science.
Send three topic preferences (unordered) to Gerrit by Monday, April 26.
Depending on the study regulations, registration/unregistration in HISPOS/LSF is due by Tuesday May 18.
Mock presentation deadline: Friday, Sep 10.
Short reports deadline: Sunday, Oct 24 (by email).

Requirements

A basic understanding of machine learning and deep learning will be helpful but is not mandatory. If you are new to the field, the 3blue1brown Neural Network Playlist might be a good start. For further consultation of general concepts, see the Deep Learning Book by Goodfellow et al. (available online). Additional material on GNNs can be found in the Graph Representation Learning Book by Hamilton (available online). There are many more good introductory GNN and ML talks on YouTube.

Grade

To pass, you have to attend all sessions, give a presentation, participate in discussions, and submit reports. Furthermore, we expect all participants to read all seminar papers in the course of the semester and write down questions/thoughts/ideas/etc. (of course, we do not expect you to understand them fully). Each participant must also give an ungraded mock presentation to their group (A, B, or C) at least 2 weeks prior to the actual presentation.

The final grade consists of: your presentation (40%), reports (40%), and participation during the seminar discussions (20%).

Presentation

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 you 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.). The presentation should be 20 to 30 minutes long.

Report

Each student has to submit four short reports (one for each paper of their group, 1000–2000 words). 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? Can you think of other useful applications?

You can use the Springer LNCS LaTeX template (every other reasonable formatting is fine, too).

Discussions

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.

Topics

Foundations

Date	Timeslot	Student	Topic	Group
23.09.2021	10:00	Safya Alzayat	Semi-Supervised Classification with Graph Convolutional Networks	A
23.09.2021	10:45	Suruthai Noon Goldstein	Inductive Representation Learning on Large Graphs	B
23.09.2021	–	Siwen Chen	Neural Message Passing for Quantum Chemistry	C

Applications

Date	Timeslot	Student	Topic	Group
23.09.2021	11:30	Gilbert El Khoury	BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis	A
23.09.2021	13:00	Divya Nidadavolu	Fake News Detection on Social Media using Geometric Deep Learning	B
23.09.2021	13:45	Roman Joeres	Modeling polypharmacy side effects with graph convolutional networks	C
24.09.2021	10:00	Maryam Meghdadi Esfahani	Neural Relational Inference for Interacting Systems	A
24.09.2021	10:45	Yan Yan Lau	Finding Patient Zero: Learning Contagion Source with Graph Neural Networks	B
24.09.2021	11:30	Aakash Rajpal	MolGAN: An implicit generative model for small molecular graphs	C

Properties, limitations, and recent advances

Date	Timeslot	Student	Topic	Group
24.09.2021	12:45	Adarsh Jamadandi	How Powerful are Graph Neural Networks?	A
24.09.2021	13:30	Vabuk Pahari	Strategies for Pre-training Graph Neural Networks	B
24.09.2021	14:15	Zhifei Li	Adversarial Attacks on Neural Networks for Graph Data	C

The participants are split into three groups (A, B, C — with four participants each). Each student writes four reports, one for each paper of their group. The mock presentations are also given within these groups.

Graph Neural Networks | Neuro-Mechanistic Modeling

Evaluation beyond goodness of fit: Quantifying biophysical alignment of AI models for kinase-centric drug discovery

Hands-On Graph Neural Networks

Lecturers

Organization

Final talktorials

Neuro-symbolic General Policy Learning

Supervisors

Content

Format

Topics

Organization

Deep Learning for Drug Discovery

Organizers

Registration

Topic

Organisation

Requirements

Grade

Presentation (50%)

Reports (30%)

Discussions (20%)

Content

Compulsory Reading List

Recommended Reading/Watch List

Background Knowledge

Paper List and Time Table

Exploring Graph Neural Networks

Registration

Topic

Organisation

Requirements

Grade

Presentation

Report

Discussions

Topics