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1708.04202
Cited By
Learning to Plan Chemical Syntheses
14 August 2017
Marwin H. S. Segler
Mike Preuss
M. Waller
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Papers citing
"Learning to Plan Chemical Syntheses"
11 / 61 papers shown
Title
SMILES-X: autonomous molecular compounds characterization for small datasets without descriptors
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The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures
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Z. Jensen
Edward J. Kim
Kevin Huang
Haw-Shiuan Chang
Emma Strubell
Jeffrey Flanigan
Andrew McCallum
E. Olivetti
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16 May 2019
A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules
Lixue Cheng
Matthew Welborn
Anders S. Christensen
Thomas F. Miller
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10 Jan 2019
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Daniil Polykovskiy
Alexander Zhebrak
Benjamín Sánchez-Lengeling
Sergey Golovanov
Oktai Tatanov
...
Simon Johansson
Hongming Chen
Sergey I. Nikolenko
Alán Aspuru-Guzik
Alex Zhavoronkov
ELM
194
633
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29 Nov 2018
GuacaMol: Benchmarking Models for De Novo Molecular Design
Nathan Brown
Marco Fiscato
Marwin H. S. Segler
Alain C. Vaucher
ELM
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691
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22 Nov 2018
Molecular Transformer - A Model for Uncertainty-Calibrated Chemical Reaction Prediction
P. Schwaller
Teodoro Laino
John McGuinness
A. Horváth
Constantine Bekas
A. Lee
25
719
0
06 Nov 2018
Compressing physical properties of atomic species for improving predictive chemistry
John E. Herr
Kevin J Koh
Kun Yao
John A. Parkhill
AI4CE
14
20
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31 Oct 2018
Latent Molecular Optimization for Targeted Therapeutic Design
Tristan Aumentado-Armstrong
15
41
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05 Sep 2018
MolGAN: An implicit generative model for small molecular graphs
Nicola De Cao
Thomas Kipf
GNN
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23
906
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30 May 2018
A Generative Model for Inverse Design of Metamaterials
Zhaocheng Liu
Dayu Zhu
S. Rodrigues
Kyu-Tae Lee
W. Cai
18
518
0
25 May 2018
A Generative Model For Electron Paths
John Bradshaw
Matt J. Kusner
Brooks Paige
Marwin H. S. Segler
José Miguel Hernández-Lobato
14
55
0
23 May 2018
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