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Curiosity in exploring chemical space: Intrinsic rewards for deep
  molecular reinforcement learning

Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning

17 December 2020
Luca Thiede
Mario Krenn
AkshatKumar Nigam
Alán Aspuru-Guzik
ArXiv (abs)PDFHTML

Papers citing "Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning"

14 / 14 papers shown
Curiosity Driven Exploration to Optimize Structure-Property Learning in Microscopy
Curiosity Driven Exploration to Optimize Structure-Property Learning in Microscopy
Aditya Vatsavai
Ganesh Narasimha
Yongtao Liu
Jan-Chi Yang
Hiroshu Funakubo
Hiroshi Funakubo
M. Ziatdinov
Rama K Vasudevan
349
1
0
28 Apr 2025
Rethinking Molecular Design: Integrating Latent Variable and
  Auto-Regressive Models for Goal Directed Generation
Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Goal Directed Generation
Heath Arthur-Loui
Amina Mollaysa
Michael Krauthammer
BDLAI4CE
316
0
0
19 Aug 2024
Geometric Active Exploration in Markov Decision Processes: the Benefit
  of Abstraction
Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction
Ric De Santi
Federico Arangath Joseph
Noah Liniger
Mirco Mutti
Andreas Krause
AI4CE
266
5
0
18 Jul 2024
Global Reinforcement Learning: Beyond Linear and Convex Rewards via
  Submodular Semi-gradient Methods
Global Reinforcement Learning: Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods
Ric De Santi
Manish Prajapat
Andreas Krause
329
13
0
13 Jul 2024
Mol-AIR: Molecular Reinforcement Learning with Adaptive Intrinsic
  Rewards for Goal-directed Molecular Generation
Mol-AIR: Molecular Reinforcement Learning with Adaptive Intrinsic Rewards for Goal-directed Molecular Generation
Jinyeong Park
Jaegyoon Ahn
Jonghwan Choi
Jibum Kim
261
13
0
29 Mar 2024
Sample Efficient Reinforcement Learning by Automatically Learning to
  Compose Subtasks
Sample Efficient Reinforcement Learning by Automatically Learning to Compose Subtasks
Shuai Han
Mehdi Dastani
Shihan Wang
OffRL
381
3
0
25 Jan 2024
Reinforcement Learning for Generative AI: State of the Art,
  Opportunities and Open Research Challenges
Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research ChallengesJournal of Artificial Intelligence Research (JAIR), 2023
Giorgio Franceschelli
Mirco Musolesi
AI4CE
821
34
0
31 Jul 2023
Recent advances in the Self-Referencing Embedding Strings (SELFIES)
  library
Recent advances in the Self-Referencing Embedding Strings (SELFIES) libraryDigital Discovery (DD), 2023
Alston Lo
R. Pollice
AkshatKumar Nigam
Andrew D. White
Mario Krenn
Alán Aspuru-Guzik
194
19
0
07 Feb 2023
Automated Gadget Discovery in Science
Automated Gadget Discovery in Science
Lea M. Trenkwalder
Andrea López-Incera
Hendrik Poulsen Nautrup
Fulvio Flamini
Hans J. Briegel
198
3
0
24 Dec 2022
On scientific understanding with artificial intelligence
On scientific understanding with artificial intelligenceNature Reviews Physics (Nat. Rev. Phys.), 2022
Mario Krenn
R. Pollice
S. Guo
Matteo Aldeghi
Alba Cervera-Lierta
...
Florian Hase
A. Jinich
AkshatKumar Nigam
Zhenpeng Yao
Alán Aspuru-Guzik
320
282
0
04 Apr 2022
SELFIES and the future of molecular string representations
SELFIES and the future of molecular string representationsPatterns (Patterns), 2022
Mario Krenn
Qianxiang Ai
Senja Barthel
Nessa Carson
Angelo Frei
...
Andrew Wang
Andrew D. White
Adamo Young
Rose Yu
A. Aspuru‐Guzik
367
228
0
31 Mar 2022
DOCKSTRING: easy molecular docking yields better benchmarks for ligand
  design
DOCKSTRING: easy molecular docking yields better benchmarks for ligand designJournal of Chemical Information and Modeling (JCIM), 2021
Miguel García-Ortegón
G. Simm
Austin Tripp
José Miguel Hernández-Lobato
A. Bender
S. Bacallado
254
108
0
29 Oct 2021
Hit and Lead Discovery with Explorative RL and Fragment-based Molecule
  Generation
Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation
Soojung Yang
Doyeong Hwang
Seul Lee
Seongok Ryu
Sung Ju Hwang
481
94
0
04 Oct 2021
Assigning Confidence to Molecular Property Prediction
Assigning Confidence to Molecular Property PredictionExpert Opinion on Drug Discovery (EODD), 2021
AkshatKumar Nigam
R. Pollice
Matthew F. D. Hurley
Riley J. Hickman
Matteo Aldeghi
Naruki Yoshikawa
Seyone Chithrananda
Vincent A. Voelz
Alán Aspuru-Guzik
AI4CE
301
51
0
23 Feb 2021
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