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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:

Pairwise Difference Regression: A Machine Learning Meta-algorithm for Improved Prediction and Uncertainty Quantification in Chemical Search
Michael Tynes, Wenhao Gao, Daniel J. Burrill, et al.
Journal of Chemical Information and Modeling (2021) Vol. 61, Iss. 8, pp. 3846-3857
Open Access | Times Cited: 45

Showing 1-25 of 45 citing articles:

Deep ensembles vs committees for uncertainty estimation in neural-network force fields: Comparison and application to active learning
Jesús Carrete, Hadrián Montes‐Campos, Ralf Wanzenböck, et al.
The Journal of Chemical Physics (2023) Vol. 158, Iss. 20
Open Access | Times Cited: 28
Uncertainty quantification by direct propagation of shallow ensembles
Matthias Kellner, Michele Ceriotti
Machine Learning Science and Technology (2024) Vol. 5, Iss. 3, pp. 035006-035006
Open Access | Times Cited: 9
DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning
Zachary Fralish, Ashley Chen, Paul Skaluba, et al.
Journal of Cheminformatics (2023) Vol. 15, Iss. 1
Open Access | Times Cited: 22
Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets
Maria H. Rasmussen, Chenru Duan, Heather J. Kulik, et al.
Journal of Cheminformatics (2023) Vol. 15, Iss. 1
Open Access | Times Cited: 18
Active Learning of Ligands That Enhance Perovskite Nanocrystal Luminescence
Min A Kim, Qianxiang Ai, Alexander J. Norquist, et al.
ACS Nano (2024) Vol. 18, Iss. 22, pp. 14514-14522
Open Access | Times Cited: 6
Uncertainty quantification: Can we trust artificial intelligence in drug discovery?
Jie Yu, Dingyan Wang, Mingyue Zheng
iScience (2022) Vol. 25, Iss. 8, pp. 104814-104814
Open Access | Times Cited: 24
Computing the relative binding affinity of ligands based on a pairwise binding comparison network
Jie Yu, Zhaojun Li, Geng Chen, et al.
Nature Computational Science (2023) Vol. 3, Iss. 10, pp. 860-872
Open Access | Times Cited: 15
Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights
Yuxinxin Chen, Yanchi Ou, Peikun Zheng, et al.
The Journal of Chemical Physics (2023) Vol. 158, Iss. 7
Closed Access | Times Cited: 12
Exploring a general convolutional neural network-based prediction model for critical casting diameter of metallic glasses
Jing Hu, Songran Yang, Jun Mao, et al.
Journal of Alloys and Compounds (2023) Vol. 947, pp. 169479-169479
Closed Access | Times Cited: 11
Exploring an accurate machine learning model to quickly estimate stability of diverse energetic materials
Qiaolin Gou, Jing Liu, Haoming Su, et al.
iScience (2024) Vol. 27, Iss. 4, pp. 109452-109452
Open Access | Times Cited: 4
Leveraging bounded datapoints to classify molecular potency improvements
Zachary Fralish, Paul Skaluba, Daniel Reker
RSC Medicinal Chemistry (2024) Vol. 15, Iss. 7, pp. 2474-2482
Open Access | Times Cited: 4
Prediction uncertainty validation for computational chemists
Pascal Pernot
The Journal of Chemical Physics (2022) Vol. 157, Iss. 14
Open Access | Times Cited: 17
Pairwise Difference Learning for Classification
Mohamed Karim Belaid, Maximilian Rabus, Eyke Hüllermeier
Lecture notes in computer science (2025), pp. 284-299
Closed Access
Simulation-based optimization of a production system topology - a neural network-assisted genetic algorithm
N. Paape, J.A.W.M. van Eekelen, M. A. Reniers
International Journal of Computer Integrated Manufacturing (2025), pp. 1-24
Open Access
Fast and Accurate Prediction of Tautomer Ratios in Aqueous Solution via a Siamese Neural Network
Xiaolin Pan, Xudong Zhang, Song Xia, et al.
Journal of Chemical Theory and Computation (2025)
Open Access
Introduction to Machine Learning for Predictive Modeling of Organic Materials
Didier Mathieu, Clément Wespiser
Challenges and advances in computational chemistry and physics (2025), pp. 43-60
Closed Access
Deep Batch Active Learning for Drug Discovery
Michael Bailey, Saeed Moayedpour, Ruijiang Li, et al.
(2024)
Open Access | Times Cited: 3
Physicochemical Responsive Integrated Similarity Measure (PRISM) for a Comprehensive Quantitative Perspective of Sample Similarity Dynamically Assessed with NIR Spectra
Robert Spiers, Callan C. Norby, John H. Kalivas
Analytical Chemistry (2023) Vol. 95, Iss. 34, pp. 12776-12784
Closed Access | Times Cited: 8
Extrapolation is not the same as interpolation
Yuxuan Wang, Ross D. King
Machine Learning (2024) Vol. 113, Iss. 10, pp. 8205-8232
Open Access | Times Cited: 2
A bioactivity foundation model using pairwise meta-learning
Bin Feng, Zequn Liu, Nan-Lan Huang, et al.
Nature Machine Intelligence (2024) Vol. 6, Iss. 8, pp. 962-974
Open Access | Times Cited: 2
Datasets, tasks, and training methods for large-scale hypergraph learning
Sun-Woo Kim, Dongjin Lee, Yul Kim, et al.
Data Mining and Knowledge Discovery (2023) Vol. 37, Iss. 6, pp. 2216-2254
Closed Access | Times Cited: 5
Extrapolation is Not the Same as Interpolation
Yuxuan Wang, Ross D. King
Lecture notes in computer science (2023), pp. 277-292
Open Access | Times Cited: 4
Analysis of machine learning prediction reliability based on sampling distance evaluation with feature decorrelation
Evan Askanazi, Ilya Grinberg
Machine Learning Science and Technology (2024) Vol. 5, Iss. 2, pp. 025030-025030
Open Access | Times Cited: 1
Active learning of ligands that enhance perovskite nanocrystal luminescence
Min A Kim, Qianxiang Ai, Alexander J. Norquist, et al.
(2024)
Open Access | Times Cited: 1
Linear Graphlet Models for Accurate and Interpretable Cheminformatics
Michael Tynes, Michael G. Taylor, Jan Janßen, et al.
Digital Discovery (2024)
Open Access | Times Cited: 1
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