On-line Lectures / paper club
"FSL-CP: Few-shot Prediction of small molecule activity using cell microscopy images" by Son Ha (17.05 .23)
"Mind the Retrosynthesis Gap: Bridging the divide between Single-step and Multi-step Retrosynthesis Prediction" by Paula Torren (5.04.23)
"Extending 3D generative modeling of molecules with quantum-mechanical properties" by Alessio Fallani (8.03.23)
"Reagent prediction with a transformer and its benefits for reaction product prediction" by Mikhail Andronov (1.02.23)
"Equivariant Graph Neural Networks for Toxicity Prediction" by Julian Cremer (1.02.23)
A tour through molecular representations in AI-driven drug discovery by Laurianne David (13.07.22)
AIDD Codebase: a Framework for Model Integration, Collaboration and Sharing by Emma Svensson and Peter Hartog (22.06.22)
Contrastive Learning of Image and Structure-Based Representations in Drug Discovery by Ana Sanchez-Fernandez (08.06.22)
Development of a CCR5 antagonist for HIV therapy by Tiago Rodrigues (27.04.22)
Hands-on: Data preparation and interactive visualization of chemical structures in KNIME Analytics Platform by Daria Goldmann (21.04.2022), meeting recording password: fv+.$@3M
A Survey on Human-in-the-loop Machine Learning by Yasmine Hahal (16.03)
3D structure refinement by Filipe Miguel Cardoso Micu Menezes (23.02.2022) see also additional files (174MB)
Coarse graining molecular dynamics with graph neural networks (paper club) by Andrey Mossyayev (27.01.2022)
AiZynthFinder: how it works and why by Samuel Genheden (20.12.2021) - meeting recording 
Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction by Mikhail Andronov (8.12.2021)
Fourth School (Aalto, March 20-23)
Collaborative modelling, design and decision making with AI, Samuel Kaski
Predicting drug combination responses in cancer, Juho Rousu
Deep latent variable models for longitudinal biomedical data, Harri Lähdesmäki
AI assistance for drug design tutorial, Elena Shaw, Sebastiaan de Peuter, Alex Hämäläinen
Efficient uncertainty estimation with node-based BNNs, Trung Trinh
Neural network potentials and beyond, Kristof Schutt
Qptuna: easy, automated QSAR model building, Lewis Mervin
Priors in Bayesian Deep Learning, Tianyu Cui
Treatment effect estimation with neural network-based models, Manuel Hausmann
Hybrid physics and AI methods for pKa predictions in proteins, Pedro Reis
Hamiltonian Monte Carlo, Aki Vehtari
Overview of the EUOS/SLAS Compound Solubility Kaggle Challenge, Wenyu Wang
OCHEM EUOS/SLAS Solubility Challenge solubility models, Igor Tetko
The Kaggle EUOS/SLAS Solubility Challenge: Visualizing and Understanding The Data Helps in Modelling, Bernhard Rohde
Third School (Leuven, October 17-26, 2022)
Use of Sampling Methods in Bayesian Inference, Christel Faes
Variational inference: from basics to modern applications, Ádám Arany
Time-to-event modelling: approaches and pitfalls, Jaak Simm
Causality-inspired ML: what can causality do for ML? The domain adaptation case, Sara Maglicane
Artificial intelligence and big data in synthetic chemistry, Timur Madzhidov (Reaxys)
Neural networks and kernel machines: the best of both worlds, Johan Suykens
Melloddy, Wouter Heyndrickx
High Content Imaging in drug discovery, Seong Joo Koo
JUMP-Cell Painting: A new public dataset to advance image-based drug discovery, Steffen Jaensch
Bidirectional Graphormer for Reactivity Understanding Neural Network Trained to Reaction Atom-to-Atom Mapping Task, Ramil Nugmanov
Multivalent interactions, Bence Bruncsics
Bayesian Deep Learning, Günter Klambauer
Molecule property prediction, federated learning and uncertainty quantification, Lewis Mervin
Materials Informatics: The Marriage of Materials and Data Sciences, Hanoch Senderowitz
Unlocking new training data sources for Drug Discovery Machine Learning, Hugo Ceulemans
Responsible Conduct of Research, how to do it, Marcel van der Heyden
The intersection of Optical Chemical Structure Recognition (OCSR) and object detection, Martijn Oldenhof
Inferring missing data with auto-associators, Mark Embrechts
Improving the effectiveness of compound design - Lessons learned from a data driven predictive platform at UCB, Marie Ledecq
Second School (Lugano, May 8-18, 2022)
Sequential decision making, RL and MDPs by (lecture_1, lecture_2) Oleg Szehr
Synthesis planning strategies by Philippe Schwaller
SELFIES: self-referencing embedded strings by Florian Häse
HPC for drug discovery by Silvano Coletti and Carmine Talaric
Artificial curiosity by Jürgen Schmidhuber
Constructing accurate machine learning force fields for flexible molecules by Leonardo Medrano
Few and zero-shot learning in drug discovery by Günter Klambauer
Experimental computational work by Mike Preuss
Magic rings: navigation in the ring chemical space guided by the bioactive ring by Peter Ertl
Geometric Deep Learning by Silvio Giancola
Artificial intelligence and the chemical space by Jean-Louis Reymond
Comparing and clustering synthetic route prediction by Samuel Genheden
Explainable AIU: interpreting, explaining and visualising deep learning (lecture 1 - contact Alessandro Facchini, lecture 2) by Alessandro Antonucci and Alessandro Facchini
Graph neural networks at the service of molecular simulations by Vittorio Limongelli
Structure based drug discovery (contact Michael Sattler) by Michael Sattler
AI formula generator by Guillaume Godin
Conformal prediction for the design problem by Clara Wong-Fannjiang
Gaussian processes and sequential design of experiments by Dario Azzimonti
Bayesian inference by Adam Arany
Cell painting assay, data analysis and reporting, and its application for identifying biological activity in new chemical matter by Axel Pahl
Overview of toxicity prediction methods by Emilio Benfenati
Explainability for molecular neural networks by Floriane Montanari
Equivariant (G)NNs by Marco Bertolini
Presentation of the "VIRTUOUS" project (contact Dario Piga) by Dario Piga and Gianvito Grasso
First School (Helmholtz Munich, October 18-29) see also Newsletter
RDkit: basics by Gregory Landram The github repo with the notebook is: https://github.com/greglandrum/AIDD_RDKit_Tutorial_2021
Workshop on PyTorch by Thomas Viehmann
Reinforcement Learning by Philipp Renz
Recurrent Neural Networks by Michael Widrich also available on https://github.com/widmi/aidd-school-2021-rnn-lstm-mhn
SMILES based modelling by Esben Jannik Bjerrum
Introduction to modeling chem reactions with ML by Marvin Segler
High Performance Computing by Martijn Oldenhof
Generative models and optimization for molecules by Rocio Mercado
Public Posters/Lectures by Fellows
2022
Towards the inverse design of molecules with targeted quantum-mechanical properties at APS March Meeting 2022, by Alessio Fallani