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Probabilistic Generative Transformer Language models for Generative
  Design of Molecules

Probabilistic Generative Transformer Language models for Generative Design of Molecules

20 September 2022
Lai Wei
Nihang Fu
Yuqi Song
Qian Wang
Jianjun Hu
    AI4CE
ArXivPDFHTML

Papers citing "Probabilistic Generative Transformer Language models for Generative Design of Molecules"

4 / 4 papers shown
Title
Keeping it Simple: Language Models can learn Complex Molecular
  Distributions
Keeping it Simple: Language Models can learn Complex Molecular Distributions
Daniel Flam-Shepherd
Kevin Zhu
A. Aspuru‐Guzik
120
142
0
06 Dec 2021
Blank Language Models
Blank Language Models
T. Shen
Victor Quach
Regina Barzilay
Tommi Jaakkola
182
73
0
08 Feb 2020
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation
  Models
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
154
628
0
29 Nov 2018
Junction Tree Variational Autoencoder for Molecular Graph Generation
Junction Tree Variational Autoencoder for Molecular Graph Generation
Wengong Jin
Regina Barzilay
Tommi Jaakkola
208
1,205
0
12 Feb 2018
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