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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:

Reinforcement Learning for Bioretrosynthesis
Mathilde Koch, Thomas Duigou, Jean‐Loup Faulon
ACS Synthetic Biology (2019) Vol. 9, Iss. 1, pp. 157-168
Open Access | Times Cited: 105

Showing 1-25 of 105 citing articles:

Machine learning for metabolic engineering: A review
Christopher E. Lawson, Jose Manuel Martí, Tijana Radivojević, et al.
Metabolic Engineering (2020) Vol. 63, pp. 34-60
Open Access | Times Cited: 198
RetroBioCat as a computer-aided synthesis planning tool for biocatalytic reactions and cascades
William Finnigan, Lorna J. Hepworth, Sabine L. Flitsch, et al.
Nature Catalysis (2021) Vol. 4, Iss. 2, pp. 98-104
Open Access | Times Cited: 185
Artificial intelligence for natural product drug discovery
Michael W. Mullowney, Katherine Duncan, Somayah S. Elsayed, et al.
Nature Reviews Drug Discovery (2023) Vol. 22, Iss. 11, pp. 895-916
Closed Access | Times Cited: 117
Designing Microbial Cell Factories for the Production of Chemicals
Jae Sung Cho, Gi Bae Kim, Hyunmin Eun, et al.
JACS Au (2022) Vol. 2, Iss. 8, pp. 1781-1799
Open Access | Times Cited: 107
Metabolic Engineering: Methodologies and Applications
Michael Volk, Vinh Tran, Shih‐I Tan, et al.
Chemical Reviews (2022) Vol. 123, Iss. 9, pp. 5521-5570
Closed Access | Times Cited: 86
Deep learning driven biosynthetic pathways navigation for natural products with BioNavi-NP
Shuangjia Zheng, Tao Zeng, Chengtao Li, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 74
Machine learning modeling of family wide enzyme-substrate specificity screens
Samuel Goldman, Ria Das, Kevin Yang, et al.
PLoS Computational Biology (2022) Vol. 18, Iss. 2, pp. e1009853-e1009853
Open Access | Times Cited: 73
Machine learning-enabled retrobiosynthesis of molecules
Tianhao Yu, Aashutosh Girish Boob, Michael Volk, et al.
Nature Catalysis (2023) Vol. 6, Iss. 2, pp. 137-151
Closed Access | Times Cited: 62
Recent progress in the synthesis of advanced biofuel and bioproducts
Brian F. Pfleger, Ralf Takors
Current Opinion in Biotechnology (2023) Vol. 80, pp. 102913-102913
Closed Access | Times Cited: 38
Automated in vivo enzyme engineering accelerates biocatalyst optimization
Enrico Orsi, Lennart Schada von Borzyskowski, Stephan Noack, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 18
Applications of artificial intelligence to enzyme and pathway design for metabolic engineering
Woo Dae Jang, Gi Bae Kim, Yeji Kim, et al.
Current Opinion in Biotechnology (2021) Vol. 73, pp. 101-107
Closed Access | Times Cited: 84
Recent advances in machine learning applications in metabolic engineering
Pradipta Patra, Disha B.R., Pritam Kundu, et al.
Biotechnology Advances (2022) Vol. 62, pp. 108069-108069
Closed Access | Times Cited: 44
Elucidating Human Milk Oligosaccharide biosynthetic genes through network-based multi-omics integration
Benjamin P. Kellman, Anne Richelle, Jeong‐Yeh Yang, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 42
Expanding biochemical knowledge and illuminating metabolic dark matter with ATLASx
Homa MohammadiPeyhani, Jasmin Hafner, Anastasia Sveshnikova, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 37
Merging enzymatic and synthetic chemistry with computational synthesis planning
Itai Levin, Mengjie Liu, Christopher A. Voigt, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 37
Developing BioNavi for Hybrid Retrosynthesis Planning
Tao Zeng, Zhehao Jin, Shuangjia Zheng, et al.
JACS Au (2024) Vol. 4, Iss. 7, pp. 2492-2502
Open Access | Times Cited: 7
Automated engineering of synthetic metabolic pathways for efficient biomanufacturing
Irene Otero‐Muras, Pablo Carbonell
Metabolic Engineering (2020) Vol. 63, pp. 61-80
Closed Access | Times Cited: 54
Chemical data intelligence for sustainable chemistry
Jana M. Weber, Zhen Guo, Chonghuan Zhang, et al.
Chemical Society Reviews (2021) Vol. 50, Iss. 21, pp. 12013-12036
Open Access | Times Cited: 41
Artificial intelligence: a solution to involution of design–build–test–learn cycle
Xiaoping Liao, Hongwu Ma, Yinjie Tang
Current Opinion in Biotechnology (2022) Vol. 75, pp. 102712-102712
Closed Access | Times Cited: 27
Machine Learning: A Suitable Method for Biocatalysis
Pedro Sampaio, Pedro Fernandes
Catalysts (2023) Vol. 13, Iss. 6, pp. 961-961
Open Access | Times Cited: 15
READRetro: natural product biosynthesis predicting with retrieval‐augmented dual‐view retrosynthesis
Taein Kim, Seul Lee, Yejin Kwak, et al.
New Phytologist (2024) Vol. 243, Iss. 6, pp. 2512-2527
Closed Access | Times Cited: 5
ALDELE: All-Purpose Deep Learning Toolkits for Predicting the Biocatalytic Activities of Enzymes
Xiangwen Wang, Derek J. Quinn, Thomas S. Moody, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 8, pp. 3123-3139
Open Access | Times Cited: 4
In silico, in vitro, and in vivo machine learning in synthetic biology and metabolic engineering
Jean‐Loup Faulon, Léon Faure
Current Opinion in Chemical Biology (2021) Vol. 65, pp. 85-92
Open Access | Times Cited: 31
When bioprocess engineering meets machine learning: A survey from the perspective of automated bioprocess development
Nghia Duong‐Trung, Stefan Born, Jong Woo Kim, et al.
Biochemical Engineering Journal (2022) Vol. 190, pp. 108764-108764
Open Access | Times Cited: 22
Microbial engineering strategies to utilize waste feedstock for sustainable bioproduction
Nikhil Aggarwal, Hoang Long Pham, Bibhuti Ranjan, et al.
Nature Reviews Bioengineering (2023) Vol. 2, Iss. 2, pp. 155-174
Closed Access | Times Cited: 12
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