Open AccessProceedings Article
Convolutional networks on graphs for learning molecular fingerprints
David Duvenaud,Dougal Maclaurin,Jorge Aguilera-Iparraguirre,Rafael Gómez-Bombarelli,Timothy D. Hirzel,Alán Aspuru-Guzik,Ryan P. Adams +6 more
- 07 Dec 2015
- Vol. 28, pp 2224-2232
TL;DR: In this paper, a convolutional neural network that operates directly on graphs is proposed to learn end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape.
read more
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.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
•Posted Content
Semi-Supervised Classification with Graph Convolutional Networks
Thomas Kipf,Max Welling +1 more
TL;DR: A scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs which outperforms related methods by a significant margin.
22.7K
Graph Attention Networks
Petar Veličković,Guillem Cucurull,Arantxa Casanova,Adriana Romero,Pietro Liò,Yoshua Bengio +5 more
- 15 Feb 2018
TL;DR: Graph Attention Networks (GATs) as mentioned in this paper leverage masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.
•Posted Content
Inductive Representation Learning on Large Graphs
TL;DR: GraphSAGE is presented, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data and outperforms strong baselines on three inductive node-classification benchmarks.
11.9K
A Comprehensive Survey on Graph Neural Networks
TL;DR: This article provides a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields and proposes a new taxonomy to divide the state-of-the-art GNNs into four categories, namely, recurrent GNNS, convolutional GNN’s, graph autoencoders, and spatial–temporal Gnns.
•Proceedings Article
Neural Message Passing for Quantum Chemistry
Justin Gilmer,Samuel S. Schoenholz,Patrick Riley,Oriol Vinyals,George E. Dahl +4 more
- 04 Apr 2017
TL;DR: The Message Passing Neural Networks (MPNNs) as mentioned in this paper are a generalization of the message passing algorithm and aggregation procedure to compute a function of their entire input graph, and have been shown to achieve state-of-the-art results on an important molecular property prediction benchmark.
References
•Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
- 01 Jan 2015
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
138.5K
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
99K
•Proceedings Article
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
- 06 Jul 2015
TL;DR: Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin.
The Graph Neural Network Model
TL;DR: A new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains, and implements a function tau(G,n) isin IRm that maps a graph G and one of its nodes n into an m-dimensional Euclidean space.
SMILES, a chemical language and information system. 1. introduction to methodology and encoding rules
TL;DR: This chapter discusses the construction of Benzenoid and Coronoid Hydrocarbons through the stages of enumeration, classification, and topological properties in a number of computers used for this purpose.
6.7K