Timezone: »

Machine Learning for Molecules
José Miguel Hernández-Lobato · Matt Kusner · Brooks Paige · Marwin Segler · Jennifer Wei

Sat Dec 12 05:30 AM -- 01:00 PM (PST) @ None
Event URL: https://ml4molecules.github.io/ »

Discovering new molecules and materials is a central pillar of human well-being, providing new medicines, securing the world’s food supply via agrochemicals, or delivering new battery or solar panel materials to mitigate climate change. However, the discovery of new molecules for an application can often take up to a decade, with costs spiraling. Machine learning can help to accelerate the discovery process. The goal of this workshop is to bring together researchers interested in improving applications of machine learning for chemical and physical problems and industry experts with practical experience in pharmaceutical and agricultural development. In a highly interactive format, we will outline the current frontiers and present emerging research directions. We aim to use this workshop as an opportunity to establish a common language between all communities, to actively discuss new research problems, and also to collect datasets by which novel machine learning models can be benchmarked. The program is a collection of invited talks, alongside contributed posters. A panel discussion will provide different perspectives and experiences of influential researchers from both fields and also engage open participant conversation. An expected outcome of this workshop is the interdisciplinary exchange of ideas and initiation of collaboration.

Author Information

José Miguel Hernández-Lobato (University of Cambridge)
Matt Kusner (University College London)
Brooks Paige (University College London)
Marwin Segler (BenevolentAI)
Jennifer Wei (Google Research)

More from the Same Authors