ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2004.12485
  4. Cited By
Learning To Navigate The Synthetically Accessible Chemical Space Using
  Reinforcement Learning

Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning

26 April 2020
S. Gottipati
B. Sattarov
Sufeng Niu
Yashaswi Pathak
Haoran Wei
Shengchao Liu
Karam M. J. Thomas
Simon R. Blackburn
Connor W. Coley
Jian Tang
Sarath Chandar
Yoshua Bengio
ArXivPDFHTML

Papers citing "Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning"

33 / 33 papers shown
Title
LLM-Augmented Chemical Synthesis and Design Decision Programs
LLM-Augmented Chemical Synthesis and Design Decision Programs
Haorui Wang
Jeff Guo
Lingkai Kong
R. Ramprasad
Philippe Schwaller
Yuanqi Du
Chao Zhang
53
0
0
11 May 2025
Entropy-Reinforced Planning with Large Language Models for Drug Discovery
Entropy-Reinforced Planning with Large Language Models for Drug Discovery
Xuefeng Liu
Chih-chan Tien
Peng Ding
Songhao Jiang
Rick L. Stevens
70
5
0
11 Jun 2024
Reinforcement Learning for Generative AI: A Survey
Reinforcement Learning for Generative AI: A Survey
Yuanjiang Cao
Quan.Z Sheng
Julian McAuley
Lina Yao
SyDa
90
11
0
28 Aug 2023
Autonomous discovery in the chemical sciences part II: Outlook
Autonomous discovery in the chemical sciences part II: Outlook
Connor W. Coley
Natalie S. Eyke
K. Jensen
39
171
0
30 Mar 2020
The Synthesizability of Molecules Proposed by Generative Models
The Synthesizability of Molecules Proposed by Generative Models
Wenhao Gao
Connor W. Coley
43
253
0
17 Feb 2020
Augmenting Genetic Algorithms with Deep Neural Networks for Exploring
  the Chemical Space
Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space
AkshatKumar Nigam
Pascal Friederich
Mario Krenn
Alán Aspuru-Guzik
AI4CE
33
131
0
25 Sep 2019
ChemBO: Bayesian Optimization of Small Organic Molecules with
  Synthesizable Recommendations
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations
Ksenia Korovina
Sailun Xu
Kirthevasan Kandasamy
Willie Neiswanger
Barnabás Póczós
J. Schneider
Eric Xing
60
122
0
05 Aug 2019
A Model to Search for Synthesizable Molecules
A Model to Search for Synthesizable Molecules
John Bradshaw
Brooks Paige
Matt J. Kusner
Marwin H. S. Segler
José Miguel Hernández-Lobato
49
108
0
12 Jun 2019
Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular
  string representation
Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation
Mario Krenn
Florian Hase
AkshatKumar Nigam
Pascal Friederich
Alán Aspuru-Guzik
74
71
0
31 May 2019
Deep learning for molecular design - a review of the state of the art
Deep learning for molecular design - a review of the state of the art
Daniel C. Elton
Zois Boukouvalas
M. Fuge
Peter W. Chung
AI4CE
3DV
55
328
0
11 Mar 2019
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
228
644
0
29 Nov 2018
DEFactor: Differentiable Edge Factorization-based Probabilistic Graph
  Generation
DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation
Rim Assouel
Mohamed Ahmed
Marwin H. S. Segler
Amir Saffari
Yoshua Bengio
49
54
0
24 Nov 2018
GuacaMol: Benchmarking Models for De Novo Molecular Design
GuacaMol: Benchmarking Models for De Novo Molecular Design
Nathan Brown
Marco Fiscato
Marwin H. S. Segler
Alain C. Vaucher
ELM
80
703
0
22 Nov 2018
Optimization of Molecules via Deep Reinforcement Learning
Optimization of Molecules via Deep Reinforcement Learning
Zhenpeng Zhou
S. Kearnes
Li Li
R. Zare
Patrick F. Riley
AI4CE
68
537
0
19 Oct 2018
N-Gram Graph: Simple Unsupervised Representation for Graphs, with
  Applications to Molecules
N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
Shengchao Liu
M. F. Demirel
Yingyu Liang
GNN
NAI
35
193
0
24 Jun 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
257
895
0
07 Jun 2018
Deep Pepper: Expert Iteration based Chess agent in the Reinforcement
  Learning Setting
Deep Pepper: Expert Iteration based Chess agent in the Reinforcement Learning Setting
Sai Krishna G.V.
Kyle Goyette
A. Chamseddine
Breandan Considine
16
3
0
02 Jun 2018
Fréchet ChemNet Distance: A metric for generative models for molecules
  in drug discovery
Fréchet ChemNet Distance: A metric for generative models for molecules in drug discovery
Kristina Preuer
Philipp Renz
Thomas Unterthiner
Sepp Hochreiter
Günter Klambauer
MedIm
76
333
0
26 Mar 2018
Addressing Function Approximation Error in Actor-Critic Methods
Addressing Function Approximation Error in Actor-Critic Methods
Scott Fujimoto
H. V. Hoof
David Meger
OffRL
139
5,121
0
26 Feb 2018
Junction Tree Variational Autoencoder for Molecular Graph Generation
Junction Tree Variational Autoencoder for Molecular Graph Generation
Wengong Jin
Regina Barzilay
Tommi Jaakkola
286
1,358
0
12 Feb 2018
GraphVAE: Towards Generation of Small Graphs Using Variational
  Autoencoders
GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders
M. Simonovsky
N. Komodakis
GNN
BDL
81
842
0
09 Feb 2018
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement
  Learning with a Stochastic Actor
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Tuomas Haarnoja
Aurick Zhou
Pieter Abbeel
Sergey Levine
194
8,236
0
04 Jan 2018
Deep Reinforcement Learning for De-Novo Drug Design
Deep Reinforcement Learning for De-Novo Drug Design
Mariya Popova
Olexandr Isayev
Alexander Tropsha
54
1,017
0
29 Nov 2017
Scalable trust-region method for deep reinforcement learning using
  Kronecker-factored approximation
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
Yuhuai Wu
Elman Mansimov
Shun Liao
Roger C. Grosse
Jimmy Ba
OffRL
33
624
0
17 Aug 2017
Learning to Plan Chemical Syntheses
Learning to Plan Chemical Syntheses
Marwin H. S. Segler
Mike Preuss
M. Waller
64
1,362
0
14 Aug 2017
Proximal Policy Optimization Algorithms
Proximal Policy Optimization Algorithms
John Schulman
Filip Wolski
Prafulla Dhariwal
Alec Radford
Oleg Klimov
OffRL
201
18,685
0
20 Jul 2017
Objective-Reinforced Generative Adversarial Networks (ORGAN) for
  Sequence Generation Models
Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
G. L. Guimaraes
Benjamín Sánchez-Lengeling
Carlos Outeiral
Pedro Luis Cunha Farias
Alán Aspuru-Guzik
GAN
62
523
0
30 May 2017
Thinking Fast and Slow with Deep Learning and Tree Search
Thinking Fast and Slow with Deep Learning and Tree Search
Thomas W. Anthony
Zheng Tian
David Barber
73
387
0
23 May 2017
Molecular De Novo Design through Deep Reinforcement Learning
Molecular De Novo Design through Deep Reinforcement Learning
Marcus Olivecrona
T. Blaschke
Ola Engkvist
Hongming Chen
BDL
92
1,003
0
25 Apr 2017
Deep Learning for Computational Chemistry
Deep Learning for Computational Chemistry
Garrett B. Goh
Nathan Oken Hodas
Abhinav Vishnu
AI4CE
45
674
0
17 Jan 2017
Automatic chemical design using a data-driven continuous representation
  of molecules
Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli
Jennifer N. Wei
David Duvenaud
José Miguel Hernández-Lobato
Benjamín Sánchez-Lengeling
Dennis Sheberla
J. Aguilera-Iparraguirre
Timothy D. Hirzel
Ryan P. Adams
Alán Aspuru-Guzik
3DV
114
2,911
0
07 Oct 2016
Deep Reinforcement Learning in Large Discrete Action Spaces
Deep Reinforcement Learning in Large Discrete Action Spaces
Gabriel Dulac-Arnold
Richard Evans
H. V. Hasselt
P. Sunehag
Timothy Lillicrap
Jonathan J. Hunt
Timothy A. Mann
T. Weber
T. Degris
Ben Coppin
OffRL
52
572
0
24 Dec 2015
Continuous control with deep reinforcement learning
Continuous control with deep reinforcement learning
Timothy Lillicrap
Jonathan J. Hunt
Alexander Pritzel
N. Heess
Tom Erez
Yuval Tassa
David Silver
Daan Wierstra
171
13,174
0
09 Sep 2015
1