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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:

A general model to predict small molecule substrates of enzymes based on machine and deep learning
Alexander Kroll, Sahasra Ranjan, Martin K. M. Engqvist, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 75

Showing 1-25 of 75 citing articles:

Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning
Alexander Kroll, Yvan Rousset, Xiao-Pan Hu, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 69
Machine Learning-Guided Protein Engineering
Petr Kouba, Pavel Kohout, Faraneh Haddadi, et al.
ACS Catalysis (2023) Vol. 13, Iss. 21, pp. 13863-13895
Open Access | Times Cited: 65
Data‐Driven Protein Engineering for Improving Catalytic Activity and Selectivity
Yu‐Fei Ao, Mark Dörr, Marian J. Menke, et al.
ChemBioChem (2023) Vol. 25, Iss. 3
Open Access | Times Cited: 19
Navigating the landscape of enzyme design: from molecular simulations to machine learning
Jiahui Zhou, Meilan Huang
Chemical Society Reviews (2024) Vol. 53, Iss. 16, pp. 8202-8239
Open Access | Times Cited: 8
A topical review on AI-interlinked biodomain sensors for multi-purpose applications
Rubi Thapa, Sachin Poudel, Katarzyna Krukiewicz, et al.
Measurement (2024) Vol. 227, pp. 114123-114123
Closed Access | Times Cited: 7
Coverage bias in small molecule machine learning
Fleming Kretschmer, Jan Seipp, Marcus Ludwig, et al.
Nature Communications (2025) Vol. 16, Iss. 1
Open Access
Simultaneous Engineering of the Thermostability and Activity of a Novel Aldehyde Dehydrogenase
Kangjie Xu, Qiming Chen, Haiyan Fu, et al.
ACS Catalysis (2025), pp. 1841-1853
Closed Access
NIRFluor: A Deep Learning Platform for Rapid Screening of Small Molecule Near-Infrared Fluorophores with Desired Optical Properties
Xiaozhi Wang, Hai‐Long Wu, Tong Wang, et al.
Analytical Chemistry (2025)
Closed Access
CPI-Pred: A deep learning framework for predicting functional parameters of compound-protein interactions
Zhiqing Xu, Rana Ahmed Barghout, Jinghao Wu, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2025)
Open Access
Machine Learning to Predict Enzyme–Substrate Interactions in Elucidation of Synthesis Pathways: A Review
Luis Fernando Salas Nuñez, Alvaro Barrera‐Ocampo, Paola A. Caicedo, et al.
Metabolites (2024) Vol. 14, Iss. 3, pp. 154-154
Open Access | Times Cited: 5
Multi-modal deep learning enables efficient and accurate annotation of enzymatic active sites
Xiaorui Wang, Xiaodan Yin, Dejun Jiang, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 5
MPEK: a multi-task learning based on pre-trained language model for predicting enzymatic reaction kinetic parameters
Hui Jiang, Jingjing Wang, Zhi-Jiang Yang, et al.
Research Square (Research Square) (2024)
Open Access | Times Cited: 4
In silico off-target profiling for enhanced drug safety assessment
Jin Liu, Yike Gui, Jingxin Rao, et al.
Acta Pharmaceutica Sinica B (2024) Vol. 14, Iss. 7, pp. 2927-2941
Open Access | Times Cited: 4
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
EITLEM-Kinetics: A deep-learning framework for kinetic parameter prediction of mutant enzymes
Xiaowei Shen, Ziheng Cui, Jianyu Long, et al.
Chem Catalysis (2024) Vol. 4, Iss. 9, pp. 101094-101094
Closed Access | Times Cited: 4
Large language models for biomolecular analysis: From methods to applications
Ruijun Feng, Chi Zhang, Yang Zhang
TrAC Trends in Analytical Chemistry (2024) Vol. 171, pp. 117540-117540
Closed Access | Times Cited: 3
Multi-Scale Protein Language Model for Unified Molecular Modeling
Kangjie Zheng, Siyu Long, Tianyu Lu, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 3
High-throughput prediction of enzyme promiscuity based on substrate–product pairs
Huadong Xing, Pengli Cai, Dongliang Liu, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 2
Open Access | Times Cited: 3
Protein Representations: Encoding Biological Information for Machine Learning in Biocatalysis
David Harding-Larsen, Jonathan Funk, Niklas Gesmar Madsen, et al.
(2024)
Open Access | Times Cited: 3
A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships
Alexander Kroll, Sahasra Ranjan, Martin J. Lercher
PLoS Computational Biology (2024) Vol. 20, Iss. 5, pp. e1012100-e1012100
Open Access | Times Cited: 3
EnzyACT: A Novel Deep Learning Method to Predict the Impacts of Single and Multiple Mutations on Enzyme Activity
Gen Li, Ning Zhang, Xiaowen Dai, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 15, pp. 5912-5921
Open Access | Times Cited: 3
FuncFetch: An LLM-assisted workflow enables mining thousands of enzyme-substrate interactions from published manuscripts
Nathaniel Smith, Xinyu Yuan, Chesney Melissinos, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 3
DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D-structures
T Wang, Guangming Xiang, Siwei He, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 5
Open Access | Times Cited: 3
DLKcat cannot predict meaningful kcat values for mutants and unfamiliar enzymes
Alexander Kroll, Martin J. Lercher
Biology Methods and Protocols (2024) Vol. 9, Iss. 1
Open Access | Times Cited: 3
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