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 about the seminar and have
[DLDDSeminar] in the subject line. For bioinformatics students, please write a mail with your questions and
[DLDDSeminar] in the subject line to Roman Joeres.
Please use the seminar assignment system to register.
We ask bioinformatic students to apply for Prof. Kalinina’s seminar. (There will be more information about this in the bioinformatics introductory lecture.)
For bioinformatic students: please apply before 04/14/2022 12pm (noon) by sending an email to Roman Joeres with
[DLDDSeminar] in the subject line. Please also attach a transcript of records from LSF.
The discovery of novel therapeutic drugs is extremely costly and time-consuming. In silico (i.e. computational) drug discovery can potentially accelerate this process. However, this method requires exploring the vast chemical space in order 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.
[DLDDSeminar]in the subject line of all emails related to the seminar.
Prior knowledge of biochemistry is not expected. General background knowledge in deep learning is recommended.
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%).
Identify the key ideas and concepts and give a self-consistent presentation explaining these concepts to your fellow students. The presentation should focus on intutivion 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 to 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
The presentation should be 25 minutes long.
Here are some suggestions for a good presentation:
Every participant has to write a short report for each paper in his/her group (except your 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).
Please email the report to your advisor.
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.
|Date (DD.MM.YYY)||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|
|09.06.2022||B||Joel Chavarria Rivera (Ilya)||Model agnostic generation of counterfactual explanations for molecules||B|
|16.06.2022||-||-||No Session - Frohnleichnam||-|
|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|
The type indicates weather a paper is suggest for bioinformatics (B) or CS sutends.
Image Credit: Tara Winstead