DeepMolGen

Deep learning for molecule generation with optimal properties

Image credits: Shutterstock

Designing new molecules is at the core of chemistry, but in practice, creating molecular structures with useful properties is a difficult and time-consuming task, simply because there are so many possibilities of connecting different atoms together to form new molecules, most of which are useless. In this project, we use a new class of machine learning techniques, called diffusion models, that is able to learn how to generate new interesting molecules. Diffusion models have already proven to be very useful in generating artistic images, movies, music, and speech, and recently also for generating pharmaceutical drugs. We will develop and apply diffusion models to (1) recover the atomistic details from low-resolution simulations of polymers, (2) optimize enzyme structures, and (3) design better catalyst materials for CO2 conversion into useful chemicals.

Participating organisations

University of Amsterdam
Netherlands eScience Center
Natural Sciences & Engineering
Natural Sciences & Engineering

Team

BE
Bernd Ensing
JD
Jacobus Dijkman
PhD student
University of Amsterdam
FH
Ferry Hooft
PhD student
University of Amsterdam
YM
Ying Ying Ma
MSc student
University of Amsterdam
Rena Bakhshi
Rena Bakhshi
Programme Manager
Netherlands eScience Center
Sonja Georgievska
Sonja Georgievska
Consultant
Netherlands eScience Center

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