Convolutional networks on graphs for learning molecular fingerprints

David Duvenaud; Dougal Maclaurin; Jorge Aguilera-Iparraguirre; Rafael Gómez-Bombarelli; Timothy Hirzel; Alán Aspuru-Guzik; Ryan P. Adams

Convolutional networks on graphs for learning molecular fingerprints

David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan P. Adams

Research output: Contribution to journal › Conference article › peer-review

2203 Scopus citations

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.

Original language	English (US)
Pages (from-to)	2224-2232
Number of pages	9
Journal	Advances in Neural Information Processing Systems
Volume	2015-January
State	Published - 2015
Externally published	Yes
Event	29th Annual Conference on Neural Information Processing Systems, NIPS 2015 - Montreal, Canada Duration: Dec 7 2015 → Dec 12 2015

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Information Systems
Signal Processing

Cite this

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title = "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.",

author = "David Duvenaud and Dougal Maclaurin and Jorge Aguilera-Iparraguirre and Rafael G{\'o}mez-Bombarelli and Timothy Hirzel and Al{\'a}n Aspuru-Guzik and Adams, \{Ryan P.\}",

year = "2015",

language = "English (US)",

volume = "2015-January",

pages = "2224--2232",

journal = "Advances in Neural Information Processing Systems",

issn = "1049-5258",

publisher = "Neural information processing systems foundation",

note = "29th Annual Conference on Neural Information Processing Systems, NIPS 2015 ; Conference date: 07-12-2015 Through 12-12-2015",

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TY - JOUR

T1 - Convolutional networks on graphs for learning molecular fingerprints

AU - Duvenaud, David

AU - Maclaurin, Dougal

AU - Aguilera-Iparraguirre, Jorge

AU - Gómez-Bombarelli, Rafael

AU - Hirzel, Timothy

AU - Aspuru-Guzik, Alán

AU - Adams, Ryan P.

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

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UR - http://www.scopus.com/inward/citedby.url?scp=84965159799&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:84965159799

SN - 1049-5258

VL - 2015-January

SP - 2224

EP - 2232

JO - Advances in Neural Information Processing Systems

JF - Advances in Neural Information Processing Systems

T2 - 29th Annual Conference on Neural Information Processing Systems, NIPS 2015

Y2 - 7 December 2015 through 12 December 2015

ER -

Convolutional networks on graphs for learning molecular fingerprints

Abstract

All Science Journal Classification (ASJC) codes

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