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:
Robustly interrogating machine learning-based scoring functions: what are they learning?
G. J. DURANT, Fergus Boyles, Kristian Birchall, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 8
G. J. DURANT, Fergus Boyles, Kristian Birchall, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 8
Showing 8 citing articles:
Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures
Anna Carbery, Martin Buttenschoen, R. Skyner, et al.
Journal of Cheminformatics (2024) Vol. 16, Iss. 1
Open Access | Times Cited: 8
Anna Carbery, Martin Buttenschoen, R. Skyner, et al.
Journal of Cheminformatics (2024) Vol. 16, Iss. 1
Open Access | Times Cited: 8
Modern machine‐learning for binding affinity estimation of protein–ligand complexes: Progress, opportunities, and challenges
Tobias Harren, Torben Gutermuth, Christoph Grebner, et al.
Wiley Interdisciplinary Reviews Computational Molecular Science (2024) Vol. 14, Iss. 3
Closed Access | Times Cited: 6
Tobias Harren, Torben Gutermuth, Christoph Grebner, et al.
Wiley Interdisciplinary Reviews Computational Molecular Science (2024) Vol. 14, Iss. 3
Closed Access | Times Cited: 6
The future of machine learning for small-molecule drug discovery will be driven by data
G. J. DURANT, Fergus Boyles, Kristian Birchall, et al.
Nature Computational Science (2024) Vol. 4, Iss. 10, pp. 735-743
Closed Access | Times Cited: 5
G. J. DURANT, Fergus Boyles, Kristian Birchall, et al.
Nature Computational Science (2024) Vol. 4, Iss. 10, pp. 735-743
Closed Access | Times Cited: 5
T-cell receptor structures and predictive models reveal comparable alpha and beta chain structural diversity despite differing genetic complexity
Nele P. Quast, Brennan Abanades, Bora Guloglu, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 4
Nele P. Quast, Brennan Abanades, Bora Guloglu, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 4
The algebraic extended atom-type graph-based model for precise ligand–receptor binding affinity prediction
Farjana Tasnim Mukta, Md Masud Rana, Avery Meyer, et al.
Journal of Cheminformatics (2025) Vol. 17, Iss. 1
Open Access
Farjana Tasnim Mukta, Md Masud Rana, Avery Meyer, et al.
Journal of Cheminformatics (2025) Vol. 17, Iss. 1
Open Access
Robust Protein-Ligand Interaction Modeling through Integrating Physical Laws and Geometric Knowledge for Absolute Binding Free Energy Calculation
Qun Su, Jike Wang, Qiaolin Gou, et al.
Chemical Science (2025)
Open Access
Qun Su, Jike Wang, Qiaolin Gou, et al.
Chemical Science (2025)
Open Access
How to make machine learning scoring functions competitive with FEP
Matthew T. Warren, ísak Valsson, Charlotte M. Deane, et al.
(2024)
Open Access | Times Cited: 2
Matthew T. Warren, ísak Valsson, Charlotte M. Deane, et al.
(2024)
Open Access | Times Cited: 2
Do Deep Learning Models for Co-Folding Learn the Physics of Protein-Ligand Interactions?
Matthew R. Masters, Amr H. Mahmoud, Markus A. Lill
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Matthew R. Masters, Amr H. Mahmoud, Markus A. Lill
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
