Lectures

Second School

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 by Dario Piga and Gianvito Grasso

First School

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