Machine Learning for Molecule Discovery

About the program 

Discovering new molecules with desired functions or activities is crucial for human well-being by providing new medicines, securing the world’s food supply via agrochemicals, or enabling a sustainable energy conversion and storage to counter or mitigate climate change. However, the discovery of new molecules or molecular materials that are optimized for a particular purpose can often take up to a decade and is highly cost-intensive. Machine-learning (ML) methods can accelerate molecular discovery, which is of considerable importance generally, but especially in light of the COVID-19 crisis and future pandemics. The program aims to establish a dialogue between domain experts and ML researchers to ensure that ML positively impacts real world scenarios. Objective: Advance computational molecular science by improving molecular representations, molecular modeling, property prediction, generative modeling for molecules and molecular optimization, and chemical synthesis through ML methods.


Workshops organized by the program

26.07.2024: ML for Life and Material Science: From Theory to Industry Applications (workshop at ICML 2024)

04.06.2024: ELLIS SYMPOSIUM on Machine Learning for Drug Discovery (organized by ELLIS Health and ELLIS ML4Molecules)

08.12.2023: Advancing Molecular Machine Learning - Overcoming Limitations [ML4Molecules]