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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:

Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias
Dávid Péter Kovács, William McCorkindale, Alpha A. Lee
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 74

Showing 1-25 of 74 citing articles:

Scientific discovery in the age of artificial intelligence
Hanchen Wang, Tianfan Fu, Yuanqi Du, et al.
Nature (2023) Vol. 620, Iss. 7972, pp. 47-60
Closed Access | Times Cited: 588
Machine learning in medical applications: A review of state-of-the-art methods
Mohammad Shehab, Laith Abualigah, Qusai Shambour, et al.
Computers in Biology and Medicine (2022) Vol. 145, pp. 105458-105458
Closed Access | Times Cited: 301
Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats
Maarten R. Dobbelaere, Pieter Plehiers, Ruben Van de Vijver, et al.
Engineering (2021) Vol. 7, Iss. 9, pp. 1201-1211
Open Access | Times Cited: 191
Explainable machine learning in materials science
Xiaoting Zhong, Brian Gallagher, Shusen Liu, et al.
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 137
Spatiotemporal pattern of greenhouse gas emissions in China’s wastewater sector and pathways towards carbon neutrality
Wenjie Du, Jia-Yuan Lu, Yi-Rong Hu, et al.
Nature Water (2023) Vol. 1, Iss. 2, pp. 166-175
Closed Access | Times Cited: 94
Machine intelligence for chemical reaction space
Philippe Schwaller, Alain C. Vaucher, Rubén Laplaza, et al.
Wiley Interdisciplinary Reviews Computational Molecular Science (2022) Vol. 12, Iss. 5
Open Access | Times Cited: 87
Retrosynthetic reaction pathway prediction through neural machine translation of atomic environments
Umit Volkan Ucak, Islambek Ashyrmamatov, Junsu Ko, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 69
Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery
Zhengkai Tu, Thijs Stuyver, Connor W. Coley
Chemical Science (2022) Vol. 14, Iss. 2, pp. 226-244
Open Access | Times Cited: 66
Retrosynthesis prediction with an interpretable deep-learning framework based on molecular assembly tasks
Y. Wang, Chao Pang, Yuzhe Wang, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 44
Accelerated chemical science with AI
Seoin Back, Alán Aspuru-Guzik, Michele Ceriotti, et al.
Digital Discovery (2023) Vol. 3, Iss. 1, pp. 23-33
Open Access | Times Cited: 37
Characterizing Uncertainty in Machine Learning for Chemistry
Esther Heid, Charles J. McGill, Florence H. Vermeire, et al.
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 13, pp. 4012-4029
Open Access | Times Cited: 36
Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE
Dávid Péter Kovács, Cas van der Oord, Jiri Kucera, et al.
Journal of Chemical Theory and Computation (2021) Vol. 17, Iss. 12, pp. 7696-7711
Open Access | Times Cited: 75
Concepts and applications of chemical fingerprint for hit and lead screening
Jingbo Yang, Yiyang Cai, Kairui Zhao, et al.
Drug Discovery Today (2022) Vol. 27, Iss. 11, pp. 103356-103356
Closed Access | Times Cited: 61
Developing green and sustainable concrete in integrating with different urban wastes
Huaguo Chen, Cheuk Lun Chow, Denvid Lau
Journal of Cleaner Production (2022) Vol. 368, pp. 133057-133057
Closed Access | Times Cited: 52
Retrosynthesis prediction using an end-to-end graph generative architecture for molecular graph editing
Weihe Zhong, Ziduo Yang, Calvin Yu‐Chian Chen
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 33
Machine learning-aided prediction of nitrogen heterocycles in bio-oil from the pyrolysis of biomass
Lijian Leng, Tanghao Li, Hao Zhan, et al.
Energy (2023) Vol. 278, pp. 127967-127967
Closed Access | Times Cited: 28
Application of machine learning in tax prediction: A review with practical approaches
Olatunji Akinrinola, Wilhelmina Afua Addy, Adeola Olusola Ajayi-Nifise, et al.
Global Journal of Engineering and Technology Advances (2024) Vol. 18, Iss. 2, pp. 102-117
Open Access | Times Cited: 8
Exploring Chemical Reaction Space with Machine Learning Models: Representation and Feature Perspective
Yuheng Ding, Bo Qiang, Qixuan Chen, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 8, pp. 2955-2970
Closed Access | Times Cited: 7
Do Chemformers Dream of Organic Matter? Evaluating a Transformer Model for Multistep Retrosynthesis
Annie M. Westerlund, Siva Manohar Koki, Supriya Kancharla, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 8, pp. 3021-3033
Closed Access | Times Cited: 6
A transferable active-learning strategy for reactive molecular force fields
Tom A. Young, Tristan Johnston-Wood, Volker L. Deringer, et al.
Chemical Science (2021) Vol. 12, Iss. 32, pp. 10944-10955
Open Access | Times Cited: 52
Explainable Solvation Free Energy Prediction Combining Graph Neural Networks with Chemical Intuition
Kaycee Low, Michelle L. Coote, Ekaterina I. Izgorodina
Journal of Chemical Information and Modeling (2022) Vol. 62, Iss. 22, pp. 5457-5470
Closed Access | Times Cited: 29
Accurate, affordable, and generalizable machine learning simulations of transition metal x-ray absorption spectra using the XANESNET deep neural network
Conor D. Rankine, Thomas J. Penfold
The Journal of Chemical Physics (2022) Vol. 156, Iss. 16
Open Access | Times Cited: 27
Making sense of mineral trace-element data – How to avoid common pitfalls in statistical analysis and interpretation
Max Frenzel
Ore Geology Reviews (2023) Vol. 159, pp. 105566-105566
Open Access | Times Cited: 15
Advances in machine learning with chemical language models in molecular property and reaction outcome predictions
Manajit Das, Ankit Ghosh, Raghavan B. Sunoj
Journal of Computational Chemistry (2024) Vol. 45, Iss. 14, pp. 1160-1176
Closed Access | Times Cited: 4
Quantum chemical data generation as fill-in for reliability enhancement of machine-learning reaction and retrosynthesis planning
Alessandra Toniato, Jan P. Unsleber, Alain C. Vaucher, et al.
Digital Discovery (2023) Vol. 2, Iss. 3, pp. 663-673
Open Access | Times Cited: 13
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