arXiv (Cornell University)· 2017· Preprint
Neural Message Passing for Quantum Chemistry
- 3,013citations
- 2017year
Short summary
Message Passing Neural Networks (MPNNs) achieve state-of-the-art results on molecular property prediction benchmarks, suggesting future work should focus on larger molecules or more accurate labels.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces Message Passing Neural Networks (MPNNs) as a unified framework for molecular symmetry-invariant neural networks.
- MPNNs achieve state-of-the-art results on a molecular property prediction benchmark.
- The current results suggest limitations are related to dataset size and ground truth accuracy, not the MPNN approach itself.
AI-generated from the title and abstract; the full text is not read.
Abstract
Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science. Luckily, several promising and closely related neural network models invariant to molecular symmetries have already been described in the literature. These models learn a message passing algorithm and aggregation procedure to compute a function of their entire input graph. At this point, the next step is to find a particularly effective variant of this general approach and apply it to chemical prediction benchmarks until we either solve them or reach the limits of the approach. In this paper, we reformulate existing models into a single common framework we call Message Passing Neural Networks (MPNNs) and explore additional novel variations within this framework. Using MPNNs we demonstrate state of the art results on an important molecular property prediction benchmark; these results are strong enough that we believe future work should focus on datasets with larger molecules or more accurate ground truth labels.
The authors' abstract, as published at the source. arXiv (Cornell University), 2017 · DOI ↗
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Field: Computational Theory and Mathematics
Computational Theory and MathematicsComputer Science