Material Science with Machine Learning



ML Conferences

IJCAI 18: Interpretable Drug Target Prediction Using Deep Neural Representation

Convolutional networks on graphs for learning molecular fingerprints.
NIPS 2015. paper Cited by 1415
David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan P. Adams.

Junction tree variational autoencoder for molecular graph generation .
ICML 2018. paper, Cited by 252
Jin, Wengong, Regina Barzilay, and Tommi Jaakkola.

Neural message passing for quantum chemistry .
ICML 17, paper Cited by 1320
Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE.

The rise of deep learning in drug discovery .
Drug discovery today 2018 . paper Cited by 409
Chen, Hongming, et al

Molecular Graph Convolutions: Moving Beyond Fingerprints.
Journal of computer-aided molecular design 2016. paper Cited by 569
Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, Patrick Riley.

Retrosynthesis Prediction with Conditional Graph Logic Network.
NeurIPS 2019. paper
Hanjun Dai, Chengtao Li, Connor Coley, Bo Dai, Le Song.

Protein Interface Prediction using Graph Convolutional Networks.
NIPS 2017. paper Cited by 171
Alex Fout, Jonathon Byrd, Basir Shariat, Asa Ben-Hur.

Graph Transformation Policy Network for Chemical Reaction Prediction.
KDD 2019. paper
Kien Do, Truyen Tran, Svetha Venkatesh.

Functional Transparency for Structured Data: a Game-Theoretic Approach.
ICML 2019. paper
Guang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi S. Jaakkola.

Learning Multimodal Graph-to-Graph Translation for Molecular Optimization. ICLR 2019. paper
Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola.

A Generative Model For Electron Paths.
ICLR 2019. paper
John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato.