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.
N1 - Funding Information:
We thank Edward Pyzer-Knapp, Jennifer Wei, and Samsung Advanced Institute of Technology for their support. This work was partially funded by NSF IIS-1421780.
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.
UR - http://www.scopus.com/inward/record.url?scp=84965159799&partnerID=8YFLogxK
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 -