Final School (Berlin, March 4 - 12, 2024)

Monday 4th March 10:45 - 11:45, PI Intros (Tetko slides, Haese slides, Clevert).

Monday 4th March 12:00 - 13:00, PI Intros (Kabeshov slides, Varnek slides, Roncaglioni slides)

Monday 4th March 15:00 - 16:00, Lecture by John O'Donnell (Bayer) Integrated Structural Biology: everything but the kitchen sink

Monday 4th March 16:00 - 17:00, Lecture by Thomas Löhr (AstraZeneca) Navigating the Maize: Computational chemistry workflows with cycles and conditions

Tuesday 5th March 10:45 - 11:30, Lecture by Anna Montebaur (Bayer) Introduction to CRISPR.

Tuesday 5th March 11:30 - 12:15, Lecture by Verena Ziegler and Sandra Berndt (Bayer) Introduction to Tox.

Tuesday 5th March 13:30 - 14:15, Lecture by Felix Oden (Bayer) Intro to targeted radiotherapies.

Tuesday 5th March 14:15 - 15:00, Lecture by Hans Briem (Bayer) Introduction to Computer-Aided Drug Design.

Wednesday 6th March 09:30 - 11:45 (short break between 10:30 and 10:45), Workshop by Gilles Marcou (UNISTRA) Intro to KNIME and Standardization of Molecules in KNIME. Materials and instructions  https://filesender.renater.fr/?s=download&token=68637050-0446-4319-aef5-732b7f53a8f8 For Window and Linux use the latest version of KNIME (5.2). For Mac users, use the version 4.5 from the official KNIME web site

Wednesday 6th March 12:00 - 15:00 (break between 13:00 and 14:00), Workshop by Alex Tropsha (UNC Eshelman School of Pharmacy) Rigor and reproducibility of Chemoinformatics models: from data curation to experimental validation

Wednesday 6th March 15:30 - 17:00 Practical Session by David Winkler (La Trobe University), Choosing an algorithm, descriptors and approach for diverse applications of AI and ML

Thursday 7th March 09:30 - 10:30, Lecture by Alessandra Roncaglioni (IRFMN), Machine Learning Models to Address Cardiotoxicity within an AOP Framework

Thursday 7th March 10:45 - 11:45, Lecture by Alexandre Tkatchenko (ULUX), Navigating Chemical Compound Space Directly and Inversely

Thursday 7th March 12:00 - 13:00, Lecture by Aixia Yan (Beijing University of Chemical Technology), Application of Machine Learning Methods for Prediction of Compound Activities and SAR Analysis

Thursday 7th March 14:00 - 15:00, Lecture by Robin Winter (Pfizer), Multi-Objective Optimization in Continuous Latent Spaces

Monday 11th March 09:30 - 13:00 (two breaks 10:30 - 10:45 and 11:30 - 11:45), GPU Programming Workshop by NVIDIA

Monday 11th March 14:00 - 18:00 (with break from 15:00 until 15:30), Project Management Workshop by Alexander Egeling

Tuesday 12th March 09:30 - 10:30, Lecture by Geemi Wellawatte (EPFL), XAI for Chemistry

Tuesday 12th March 11:10 - 12:10, Lecture by Mike Preuss (ULEI) Monte Carlo tree search and multi-objective variants

Tuesday 12th March 14:00 - 14:30, AIDD Presentations ESR 1 - 4

Tuesday 12th March 14:30 - 17:00, AIDD Presentations ESR 5 -15

See also AiChemist School lectures (13-15 March)

On-line Lectures / paper club

Mikhail Andronov gave a talk based on his recently uploaded pre-print, titled "A reagent-driven visual method for analyzing chemical reaction data" (21.02.24)

Julian Cremer gave an online lecture  titled "Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation" (12.12.23)

Arslan Masood gave an online lecture titled "Dissecting Drug-Induced-Liver-Injury" (28.11.23)

Rosa Friesacher gave an online lecture, titled "Understanding Model Uncertainty and Enhancing Probability Calibration in Neural Networks" (25.10.23)

Varvara Voinarovska gave an online lecture titled “When yield prediction does not yield prediction: an overview of the current challenges” (19.09.23)

Mariia Radaeva gave lecture "Targeting Androgen Receptor with CADD" (19.07.23)

On-line presentation "Science is fun but not only: career perspectives for young talented researchers in a modern society" with focus on opportunities in European Union and Germany by Igor Tetko (11.07.23)

"Success stories of structure-based drug discovery" by Ana Messias  (22.06.23)

"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  YouTube

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

Fifth School (Gothenburg, July 3-7, 2023)

AI from a MedChem perspective, Werngard Czechtizky

Predicting reaction selectivity, Per-Ola Norrby 

Overcoming safety liabilities with machine learning - An industry perspective, Vignesh Subramanian

Journey of Developing AI solutions for Digital Pathology Data, Feng Gu 

The promise of graphs & graph-based learning in drug discovery, Ufuk Kirik 

Data-driven enhanced sampling of conformational changes in membrane proteins, Lucie Delemotte 

Knowledge graphs, Michaël Ughetto 

The new journey on AI modelling after REINVENT, Hongming Chen 

REINVENT+CAZP workshop (interactive)

Introduction to iLab, Tove Slagbrand 

LLM, Chemistry, and the Future of AI, Mike Preuss 

Introduction AI Sweden, Emma Ytterström 

Privacy preserving ML at AI Sweden, Johan Östman 


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 LearningTianyu 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)

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 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


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