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2015 NIPS NeurIPS 2015

Convolutional Networks on Graphs for Learning Molecular Fingerprints

Abstract

We introduce a convolutional neural network that operates directly on graphs.These networks allow end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape.The architecture we present generalizes standard molecular feature extraction methods based on circular fingerprints.We show that these data-driven features are more interpretable, and have better predictive performance on a variety of tasks.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning
📈 Trend Setter — Graph Neural Networks
🧭 Keyword Pioneer — molecular fingerprint
🐣 Hot Topic Early Bird — end-to-end learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

David K. Duvenaud , Dougal Maclaurin , Jorge Iparraguirre , Rafael Bombarell , Timothy Hirzel , Alan Aspuru-Guzik , Ryan P. Adams

Topics

Machine Learning > Core Methods > Representation Learning Machine Learning > Application Areas > Efficient Computing Deep Learning > Architectures > Graph Neural Networks

Keywords

feature learning convolutional neural network end-to-end learning molecular property prediction graph neural network molecular fingerprint circular fingerprint
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