Drug Inpainting with Machine Learning

Antibiotic resistance represents one of the major threats to human health. The adaptation of pathogen requires a constant effort to find new drug and targets. The development of new drugs is becoming increasingly complex. To improve the efficiency and drive down cost, the drug discovery effort relies more and more on computational techniques to screen through large library of small molecules.

In this project, we propose to use machine learning for the discovery of new drugs. The datasets are composed of 3 dimensional structure of complexes. The candidate will have to identify the best representation(s) for encoding these complexes and key physicochemical properties that should be considered.

Supervised by Thomas Lemmin