Timezone: »
Molecular property prediction is one of the fastest-growing applications of deep learning with critical real-world impacts. Including 3D molecular structure as input to learned models improves their predictions for many molecular properties. However, this information is infeasible to compute at the scale required by most real-world applications. We propose pre-training a model to understand the geometry of molecules given only their 2D molecular graph. Using methods from self-supervised learning, we maximize the mutual information between a 3D summary vector and the representations of a Graph Neural Network (GNN) such that they contain latent 3D information. During fine-tuning on molecules with unknown geometry, the GNN still generates implicit 3D information and can use it to inform downstream tasks. We show that 3D pre-training provides significant improvements for a wide range of molecular properties, such as a 22% average MAE reduction on eight quantum mechanical properties. Crucially, the learned representations can be effectively transferred between datasets with vastly different molecules.
Author Information
Hannes Stärk (Technical University of Munich)
Gabriele Corso (MIT)
Christian Dallago (Technical University of Munich)
Stephan Günnemann (Technical University of Munich)
Pietro Lió (University of Cambridge)
More from the Same Authors
-
2021 : FLIP: Benchmark tasks in fitness landscape inference for proteins »
Christian Dallago · Jody Mou · Kadina Johnston · Bruce Wittmann · Nicholas Bhattacharya · Samuel Goldman · Ali Madani · Kevin Yang -
2021 : Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience »
Johannes C. Paetzold · Julian McGinnis · Suprosanna Shit · Ivan Ezhov · Paul Büschl · Chinmay Prabhakar · Anjany Sekuboyina · Mihail Todorov · Georgios Kaissis · Ali Ertürk · Stephan Günnemann · Bjoern Menze -
2021 : Interpretable Data Analysis for Bench-to-Bedside Research »
Zohreh Shams · Botty Dimanov · Nikola Simidjievski · Helena Andres-Terre · Paul Scherer · Urška Matjašec · Mateja Jamnik · Pietro Lió -
2021 : Structure-aware generation of drug-like molecules »
Pavol Drotar · Arian Jamasb · Ben Day · Catalina Cangea · Pietro Lió -
2021 : 3D Pre-training improves GNNs for Molecular Property Prediction »
Hannes Stärk · Dominique Beaini · Gabriele Corso · Prudencio Tossou · Christian Dallago · Stephan Günnemann · Pietro Lió -
2021 : Approximate Latent Force Model Inference »
Jacob Moss · Felix Opolka · Pietro Lió -
2021 : Learning Graph Search Heuristics »
Michal Pándy · Rex Ying · Gabriele Corso · Petar Veličković · Jure Leskovec · Pietro Liò -
2021 : Neural ODE Processes: A Short Summary »
Alexander Norcliffe · Cristian Bodnar · Ben Day · Jacob Moss · Pietro Lió -
2021 : On Second Order Behaviour in Augmented Neural ODEs: A Short Summary »
Alexander Norcliffe · Cristian Bodnar · Ben Day · Nikola Simidjievski · Pietro Lió -
2021 : Structure-aware generation of drug-like molecules »
Pavol Drotar · Arian Jamasb · Ben Day · Catalina Cangea · Pietro Lió -
2021 Poster: Robustness of Graph Neural Networks at Scale »
Simon Geisler · Tobias Schmidt · Hakan Şirin · Daniel Zügner · Aleksandar Bojchevski · Stephan Günnemann -
2021 Poster: Directional Message Passing on Molecular Graphs via Synthetic Coordinates »
Johannes Gasteiger · Chandan Yeshwanth · Stephan Günnemann -
2021 Poster: Neural Flows: Efficient Alternative to Neural ODEs »
Marin Biloš · Johanna Sommer · Syama Sundar Rangapuram · Tim Januschowski · Stephan Günnemann -
2021 Poster: Detecting Anomalous Event Sequences with Temporal Point Processes »
Oleksandr Shchur · Ali Caner Turkmen · Tim Januschowski · Jan Gasthaus · Stephan Günnemann -
2021 Poster: GemNet: Universal Directional Graph Neural Networks for Molecules »
Johannes Gasteiger · Florian Becker · Stephan Günnemann -
2021 Poster: Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification »
Maximilian Stadler · Bertrand Charpentier · Simon Geisler · Daniel Zügner · Stephan Günnemann -
2020 Poster: Constraining Variational Inference with Geometric Jensen-Shannon Divergence »
Jacob Deasy · Nikola Simidjievski · Pietro Lió -
2020 Poster: Fast and Flexible Temporal Point Processes with Triangular Maps »
Oleksandr Shchur · Nicholas Gao · Marin Biloš · Stephan Günnemann -
2020 Poster: On Second Order Behaviour in Augmented Neural ODEs »
Alexander Norcliffe · Cristian Bodnar · Ben Day · Nikola Simidjievski · Pietro Lió -
2020 Poster: Deep Rao-Blackwellised Particle Filters for Time Series Forecasting »
Richard Kurle · Syama Sundar Rangapuram · Emmanuel de Bézenac · Stephan Günnemann · Jan Gasthaus -
2020 Poster: Reliable Graph Neural Networks via Robust Aggregation »
Simon Geisler · Daniel Zügner · Stephan Günnemann -
2020 Oral: Fast and Flexible Temporal Point Processes with Triangular Maps »
Oleksandr Shchur · Nicholas Gao · Marin Biloš · Stephan Günnemann -
2020 Poster: Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts »
Bertrand Charpentier · Daniel Zügner · Stephan Günnemann -
2019 Poster: Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift »
Stephan Rabanser · Stephan Günnemann · Zachary Lipton -
2019 Poster: Diffusion Improves Graph Learning »
Johannes Gasteiger · Stefan Weißenberger · Stephan Günnemann -
2019 Poster: Uncertainty on Asynchronous Time Event Prediction »
Marin Biloš · Bertrand Charpentier · Stephan Günnemann -
2019 Spotlight: Uncertainty on Asynchronous Time Event Prediction »
Marin Biloš · Bertrand Charpentier · Stephan Günnemann -
2019 Poster: Certifiable Robustness to Graph Perturbations »
Aleksandar Bojchevski · Stephan Günnemann