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Sat Dec 12 05:30 AM -- 01:00 PM (PST)
Machine Learning for Molecules
José Miguel Hernández-Lobato · Matt Kusner · Brooks Paige · Marwin Segler · Jennifer Wei

Workshop Home Page

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.

Opening Remarks
Discord for Q&A (Q&A)
Invited Talk: Nadine Schneider -Real-world application of ML in drug discovery (Talk)
Invited Talk: Nadine Schneider - Live Q&A (Q&A)
Invited Talk: Frank Noe - The sampling problem in statistical mechanics and Boltzmann-Generating Flows (Talk)
Invited Talk: Frank Noe - Live Q&A (Q&A)
Contributed Talk: Evidential Deep Learning for Guided Molecular Property Prediction and Discovery - Ava Soleimany, Alexander Amini, Samuel Goldman, Daniela Rus, Sangeeta Bhatia and Connor Coley (Talk)
Contributed Talk: Gaussian Process Molecular Property Prediction with FlowMO - Henry Moss and Ryan-Rhys Griffiths (Talk)
Contributed Talk: Explaining Deep Graph Networks with Molecular Counterfactuals - Davide Bacciu and Danilo Numeroso (Talk)
Invited Talk: Klaus Robert-Müller & Kristof Schütt: Machine Learning meets Quantum Chemistry (Talk)
Invited Talk: Klaus Robert-Müller and Kristof Schütt - Live Q&A (Q&A)
Invited Talk: Rocio Mercado - Applying Graph Neural Networks to Molecular Design (Talk)
Invited Talk: Rocio Mercado - Live Q&A (Q&A)
Spotlight Talk: Comparison of Atom Representations in Graph Neural Networks for Molecular Property Prediction - Agnieszka Pocha, Tomasz Danel and Lukasz Maziarka (Talk)
Spotlight Talk: Completion of partial reaction equations - Alain C. Vaucher, Philippe Schwaller and Teodoro Laino (Talk)
Spotlight Talk: Molecular representation learning with language models and domain-relevant auxiliary tasks - Benedek Fabian, Thomas Edlich, Héléna Gaspar, Marwin Segler, Joshua Meyers, Marco Fiscato and Mohamed Ahmed (Talk)
Spotlight Talk: Accelerate the screening of complex materials by learning to reduce random and systematic errors - Tian Xie, Yang Shao-Horn and Jeffrey Grossman. (Talk)
Poster Session Break (Break)
Panel (Discussion Panel)
Contributed Talk: Bayesian GNNs for Molecular Property Prediction - George Lamb and Brooks Paige (Talk)
Contributed Talk: Design of Experiments for Verifying Biomolecular Networks - Ruby Sedgwick, John Goertz, Ruth Misener, Molly Stevens and Mark van der Wilk. (Talk)
Contributed Talk: Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces - Z. Qiao, F. Ding, M. Welborn, P.J. Bygrave, D.G.A. Smith, A. Anandkumar, F. R. Manby and TF. Miller III (Talk)
Invited Talk: Patrick Walters - Challenges and Opportunities for Machine Learning in Drug Discovery (Talk)
Invited Talk: Patrick Walters - Live Q&A (Q&A)
Invited Talk: Yannick Djoumbou Feunang - In Silico Prediction and Identification of Metabolites with BioTransformer (Talk)
Invited Talk: Yannick Djoumbou Feunang - Live Q&A (Q&A)
Spotlight Talk: Data augmentation strategies to improve reaction yield predictions and estimate uncertainty - Philippe Schwaller, Alain Vaucher, Teodoro Laino and Jean-Louis Reymond (Talk)
Spotlight Talk: Message Passing Networks for Molecules with Tetrahedral Chirality - Lagnajit Pattanaik, Octavian Ganea, Ian Coley, Klavs Jensen, William Green and Connor Coley. (Talk)
Spotlight Talk: Protein model quality assessment using rotation-equivariant, hierarchical neural networks - Stephan Eismann, Patricia Suriana, Bowen Jing, Raphael Townshend and Ron Dror. (Talk)
Spotlight Talk: Crystal Structure Search with Random Relaxations Using Graph Networks - Gowoon Cheon, Lusann Yang, Kevin McCloskey, Evan Reed and Ekin Cubuk (Talk)
Invited Talk: Benjamin Sanchez-Lengeling - Evaluating Attribution of Molecules with Graph Neural Networks (Talk)
Invited Talk: Benjamin Sanchez-Lengeling - Live Q&A (Q&A)
Invited Talk: Jennifer Listgarten (Talk)
Invited Talk: Jennifer Listgarten - Live Q&A (Q&A)
Closing Remarks
Poster Session Part 2 (Break)