Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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 26-50 of 75 citing articles:

DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D-structures
Tong 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
Cell factory design with advanced metabolic modelling empowered by artificial intelligence
Hongzhong Lu, Luchi Xiao, Wenbin Liao, et al.
Metabolic Engineering (2024) Vol. 85, pp. 61-72
Open Access | Times Cited: 3
A General Machine Learning Model for Assessing Fruit Quality Using Deep Image Features
Ioannis D. Apostolopoulos, Mpesi Tzani, Sokratis I. Aznaouridis
AI (2023) Vol. 4, Iss. 4, pp. 812-830
Open Access | Times Cited: 8
PocketGen: Generating Full-Atom Ligand-Binding Protein Pockets
Zaixi Zhang, Wan Xiang Shen, Qi Liu, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 2
Evaluating generalizability of artificial intelligence models for molecular datasets
Yasha Ektefaie, Andrew Shen, Daria Bykova, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 2
Beyond co-expression: pathway discovery for plant pharmaceuticals
Sandesh H Swamidatta, Benjamin R. Lichman
Current Opinion in Biotechnology (2024) Vol. 88, pp. 103147-103147
Open Access | Times Cited: 2
Investigation of in silico studies for cytochrome P450 isoforms specificity
Yao Wei, Luca Palazzolo, Omar Ben Mariem, et al.
Computational and Structural Biotechnology Journal (2024) Vol. 23, pp. 3090-3103
Open Access | Times Cited: 2
Machine learning models for the prediction of enzyme properties should be tested on proteins not used for model training
Alexander Kroll, Martin J. Lercher
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 7
Small molecule machine learning: All models are wrong, some may not even be useful
Fleming Kretschmer, Jan Seipp, Marcus Ludwig, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 7
Machine Learning Enables Prediction of Pyrrolysyl-tRNA Synthetase Substrate Specificity
Qunfeng Zhang, Wenlong Zheng, Zhongdi Song, et al.
ACS Synthetic Biology (2023) Vol. 12, Iss. 8, pp. 2403-2417
Closed Access | Times Cited: 7
Deep learning for metabolic pathway design
G.U. Ryu, Gi Bae Kim, Taeho Yu, et al.
Metabolic Engineering (2023) Vol. 80, pp. 130-141
Closed Access | Times Cited: 5
Decoding the molecular interplay in the central dogma: An overview of mass spectrometry‐based methods to investigate protein‐metabolite interactions
Paolo Stincone, Amira Naimi, Anthony J. Saviola, et al.
PROTEOMICS (2023) Vol. 24, Iss. 12-13
Open Access | Times Cited: 5
Indoles and the advances in their biotechnological production for industrial applications
Lenny Ferrer, Melanie Mindt, Volker F. Wendisch, et al.
Systems Microbiology and Biomanufacturing (2023) Vol. 4, Iss. 2, pp. 511-527
Open Access | Times Cited: 5
Friends and relatives: insight into conformational regulation from orthologues and evolutionary lineages using KIF and KIN
Dariia Yehorova, Rory Crean, Peter M. Kasson, et al.
Faraday Discussions (2024) Vol. 252, pp. 341-353
Open Access | Times Cited: 1
Descriptor-augmented machine learning for enzyme-chemical interaction predictions
Yilei Han, Haoye Zhang, Zheni Zeng, et al.
Synthetic and Systems Biotechnology (2024) Vol. 9, Iss. 2, pp. 259-268
Open Access | Times Cited: 1
Investigating Enzyme Biochemistry by Deep Learning: A Computational Tool for a New Era
Milad Rayka, Morteza Mirzaei, Gholamreza Farnoosh, et al.
Journal of Computational Biophysics and Chemistry (2024) Vol. 23, Iss. 06, pp. 781-799
Closed Access | Times Cited: 1
Machine learning for predicting protein properties: A comprehensive review
Yizhen Wang, Yanyun Zhang, Xuhui Zhan, et al.
Neurocomputing (2024) Vol. 597, pp. 128103-128103
Closed Access | Times Cited: 1
VIPER: A General Model for Prediction of Enzyme Substrates
Maria Jose Campbell
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Closed Access | Times Cited: 1
Introducing enzymatic cleavage features and transfer learning realizes accurate peptide half-life prediction across species and organs
Xiaorong Tan, Qianhui Liu, Yanpeng Fang, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 4
Open Access | Times Cited: 1
A Machine Learning-Guided Approach to Navigate the Substrate Activity Scope of Galactose Oxidase: Application in the Conversion of Pharmaceutically Relevant Bulky Secondary Alcohols
Shreyas Supekar, Dillon W. P. Tay, Wan Lin Yeo, et al.
(2024)
Open Access | Times Cited: 1
SPOT: A machine learning model that predicts specific substrates for transport proteins
Alexander Kroll, Nico Niebuhr, Gregory Butler, et al.
PLoS Biology (2024) Vol. 22, Iss. 9, pp. e3002807-e3002807
Open Access | Times Cited: 1
Protein representations: Encoding biological information for machine learning in biocatalysis
David Harding-Larsen, Jonathan Funk, Niklas Gesmar Madsen, et al.
Biotechnology Advances (2024) Vol. 77, pp. 108459-108459
Closed Access | Times Cited: 1
Evaluating generalizability of artificial intelligence models for molecular datasets
Yasha Ektefaie, Andrew Shen, Daria Bykova, et al.
Nature Machine Intelligence (2024)
Closed Access | Times Cited: 1
Drug-target interaction prediction using a multi-modal transformer network demonstrates high generalizability to unseen proteins
Alexander Kroll, Sahasra Ranjan, Martin J. Lercher
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 4

Scroll to top