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Beam Enumeration: Probabilistic Explainability For Sample Efficient
  Self-conditioned Molecular Design

Beam Enumeration: Probabilistic Explainability For Sample Efficient Self-conditioned Molecular Design

25 September 2023
Jeff Guo
P. Schwaller
ArXivPDFHTML

Papers citing "Beam Enumeration: Probabilistic Explainability For Sample Efficient Self-conditioned Molecular Design"

4 / 4 papers shown
Title
Explainable AI: current status and future directions
Explainable AI: current status and future directions
Prashant Gohel
Priyanka Singh
M. Mohanty
XAI
79
86
0
12 Jul 2021
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
167
628
0
29 Nov 2018
Graph Convolutional Policy Network for Goal-Directed Molecular Graph
  Generation
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
Jiaxuan You
Bowen Liu
Rex Ying
Vijay S. Pande
J. Leskovec
GNN
184
878
0
07 Jun 2018
Junction Tree Variational Autoencoder for Molecular Graph Generation
Junction Tree Variational Autoencoder for Molecular Graph Generation
Wengong Jin
Regina Barzilay
Tommi Jaakkola
211
1,329
0
12 Feb 2018
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