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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Augmented Hill-Climb increases reinforcement learning efficiency for language-based de novo molecule generation
Morgan Thomas, N.M. O'Boyle, Andreas Bender, et al.
Journal of Cheminformatics (2022) Vol. 14, Iss. 1
Open Access | Times Cited: 20

Showing 20 citing articles:

Integrating structure-based approaches in generative molecular design
Morgan Thomas, Andreas Bender, Chris de Graaf
Current Opinion in Structural Biology (2023) Vol. 79, pp. 102559-102559
Open Access | Times Cited: 39
Sample efficient reinforcement learning with active learning for molecular design
Michael Dodds, Jeff Guo, Thomas Löhr, et al.
Chemical Science (2024) Vol. 15, Iss. 11, pp. 4146-4160
Open Access | Times Cited: 11
Utilizing reinforcement learning for de novo drug design
Hampus Gummesson Svensson, Christian Tyrchan, Ola Engkvist, et al.
Machine Learning (2024) Vol. 113, Iss. 7, pp. 4811-4843
Open Access | Times Cited: 5
A beginner’s approach to deep learning applied to VS and MD techniques
Stijn D'Hondt, José Oramas, Hans De Winter
Journal of Cheminformatics (2025) Vol. 17, Iss. 1
Open Access
MolScore: a scoring, evaluation and benchmarking framework for generative models in de novo drug design
Morgan Thomas, N.M. O'Boyle, Andreas Bender, et al.
Journal of Cheminformatics (2024) Vol. 16, Iss. 1
Open Access | Times Cited: 4
Discovery and Evaluation of 3-Quinoxalin Urea Derivatives as Potent, Selective, and Orally Available ATM Inhibitors Combined with Chemotherapy for the Treatment of Cancer via Goal-Oriented Molecule Generation and Virtual Screening
Dexin Deng, Yingxue Yang, Yurong Zou, et al.
Journal of Medicinal Chemistry (2023) Vol. 66, Iss. 14, pp. 9495-9518
Closed Access | Times Cited: 10
Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning
Jeff Guo, Philippe Schwaller
JACS Au (2024) Vol. 4, Iss. 6, pp. 2160-2172
Open Access | Times Cited: 3
On the difficulty of validating molecular generative models realistically: a case study on public and proprietary data
Koichi Handa, Morgan Thomas, Michiharu Kageyama, et al.
Journal of Cheminformatics (2023) Vol. 15, Iss. 1
Open Access | Times Cited: 8
ACEGEN: Reinforcement Learning of Generative Chemical Agents for Drug Discovery
Albert Bou, Morgan Thomas, Sebastian Dittert, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 15, pp. 5900-5911
Open Access | Times Cited: 2
PromptSMILES: prompting for scaffold decoration and fragment linking in chemical language models
Morgan Thomas, Mazen Ahmad, Gary Tresadern, et al.
Journal of Cheminformatics (2024) Vol. 16, Iss. 1
Open Access | Times Cited: 1
Augmented Memory: Capitalizing on Experience Replay to Accelerate De Novo Molecular Design
Jeff Guo, Philippe Schwaller
(2023)
Open Access | Times Cited: 4
Sample Efficient Reinforcement Learning with Active Learning for Molecular Design
Michael Dodds, Jeff Guo, Thomas Löhr, et al.
(2023)
Open Access | Times Cited: 4
PromptSMILES: Prompting for scaffold decoration and fragment linking in chemical language models
Morgan Thomas, Mazen Ahmad, Gary Tresadern, et al.
(2024)
Open Access | Times Cited: 1
Diverse Hits in De Novo Molecule Design: Diversity-Based Comparison of Goal-Directed Generators
Philipp Renz, Sohvi Luukkonen, Günter Klambauer
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 15, pp. 5756-5761
Open Access | Times Cited: 1
Inhibitor_Mol_VAE: a variational autoencoder approach for generating corrosion inhibitor molecules
Haiyan Gong, Zhongheng Fu, Lingwei Ma, et al.
npj Materials Degradation (2024) Vol. 8, Iss. 1
Open Access | Times Cited: 1
Examining multi-objective deep reinforcement learning frameworks for molecular design
Aws Al-Jumaily, Muhetaer Mukaidaisi, Andrew Vu, et al.
Biosystems (2023) Vol. 232, pp. 104989-104989
Closed Access | Times Cited: 3
Augmented Memory: Capitalizing on Experience Replay to Accelerate De Novo Molecular Design
Jeff Guo, Philippe Schwaller
(2023)
Open Access | Times Cited: 2
On The Difficulty of Validating Molecular Generative Models Realistically: A Case Study on Public and Proprietary Data
Koichi Handa, Morgan Thomas, Michiharu Kageyama, et al.
(2023)
Open Access | Times Cited: 2
A systematic review of deep learning chemical language models in recent era
Hector Flores-Hernandez, Emmanuel Martínez-Ledesma
Journal of Cheminformatics (2024) Vol. 16, Iss. 1
Open Access
LOGICS: Learning optimal generative distribution for designing de novo chemical structures
Bongsung Bae, Haelee Bae, Hojung Nam
Journal of Cheminformatics (2023) Vol. 15, Iss. 1
Open Access | Times Cited: 1
Augmented Memory: Capitalizing on Experience Replay to Accelerate De Novo Molecular Design
Jeff Guo, Philippe Schwaller
(2023)
Open Access
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