On-line Lectures / paper club

A tour through molecular representations in AI-driven drug discovery by Laurianne David (13.07.22)

AIDD Seminar: AIDD Codebase: a Framework for Model Integration, Collaboration and Sharing by Emma Svensson and Peter Hartog (22.06.22) - only for AIDD fellows

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

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  YouTube

Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction (paper club) by Mikhail Andronov (8.12.2021)


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)

Ethics of AI, Yves Moreau

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

Cell image, Srijit Seal

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 itMarcel 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


Towards the inverse design of molecules with targeted quantum-mechanical properties at APS March Meeting 2022,   by Alessio Fallani