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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:

Artificial Intelligence in Chemistry: Current Trends and Future Directions
Zachary J. Baum, Xiang Yu, Philippe Y. Ayala, et al.
Journal of Chemical Information and Modeling (2021) Vol. 61, Iss. 7, pp. 3197-3212
Closed Access | Times Cited: 182

Showing 1-25 of 182 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: 741
The transformational role of GPU computing and deep learning in drug discovery
Mohit Pandey, Michael Fernández, Francesco Gentile, et al.
Nature Machine Intelligence (2022) Vol. 4, Iss. 3, pp. 211-221
Open Access | Times Cited: 179
Modulating the microenvironment of single atom catalysts with tailored activity to benchmark the CO2 reduction
Saira Ajmal, Anuj Kumar, Mohammad Tabish, et al.
Materials Today (2023) Vol. 67, pp. 203-228
Closed Access | Times Cited: 73
Machine learning for design principles for single atom catalysts towards electrochemical reactions
Mohsen Tamtaji, Hanyu Gao, Md Delowar Hossain, et al.
Journal of Materials Chemistry A (2022) Vol. 10, Iss. 29, pp. 15309-15331
Open Access | Times Cited: 70
New avenues in artificial-intelligence-assisted drug discovery
Carmen Cerchia, Antonio Lavecchia
Drug Discovery Today (2023) Vol. 28, Iss. 4, pp. 103516-103516
Open Access | Times Cited: 59
ChatGPT Research Group for Optimizing the Crystallinity of MOFs and COFs
Zhiling Zheng, Oufan Zhang, Ha L. Nguyen, et al.
ACS Central Science (2023) Vol. 9, Iss. 11, pp. 2161-2170
Open Access | Times Cited: 59
Artificial intelligence adoption in the physical sciences, natural sciences, life sciences, social sciences and the arts and humanities: A bibliometric analysis of research publications from 1960-2021
Stefan Hajkowicz, Conrad Sanderson, Sarvnaz Karimi, et al.
Technology in Society (2023) Vol. 74, pp. 102260-102260
Open Access | Times Cited: 58
Sulfur fluoride exchange
Joshua A. Homer, Long Xu, Namitharan Kayambu, et al.
Nature Reviews Methods Primers (2023) Vol. 3, Iss. 1
Closed Access | Times Cited: 56
Revolutionizing clinical trials: the role of AI in accelerating medical breakthroughs
Hitesh Chopra, Annu Annu, Dong Kyoo Shin, et al.
International Journal of Surgery (2023) Vol. 109, Iss. 12, pp. 4211-4220
Open Access | Times Cited: 49
Artificial intelligence and water quality: From drinking water to wastewater
Christian Hazael Pérez-Beltrán, Alicia Robles, N. Rodríguez, et al.
TrAC Trends in Analytical Chemistry (2024) Vol. 172, pp. 117597-117597
Closed Access | Times Cited: 18
Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptors
Miguel Gallegos, Valentín Vassilev-Galindo, Igor Poltavsky, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 17
Trends, Challenges, and Research Pathways in Emerging Contaminants: A Comprehensive Bibliometric Analysis
Md Shahin Alam, Farzaneh Tahriri, Gang Chen
Integrated Environmental Assessment and Management (2025)
Closed Access | Times Cited: 7
Harnessing the Power of Artificial Intelligence in Pharmaceuticals: Current Trends and Future Prospects
Saha Aritra, Indu Singh
Intelligent Pharmacy (2025)
Open Access | Times Cited: 2
User trust in artificial intelligence: A comprehensive conceptual framework
Rongbin Yang, Santoso Wibowo
Electronic Markets (2022) Vol. 32, Iss. 4, pp. 2053-2077
Closed Access | Times Cited: 58
Merging enzymatic and synthetic chemistry with computational synthesis planning
Itai Levin, Mengjie Liu, Christopher A. Voigt, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 41
Advances and applications of machine learning and deep learning in environmental ecology and health
Shixuan Cui, Yuchen Gao, Yizhou Huang, et al.
Environmental Pollution (2023) Vol. 335, pp. 122358-122358
Closed Access | Times Cited: 36
A Review on Artificial Intelligence Enabled Design, Synthesis, and Process Optimization of Chemical Products for Industry 4.0
Chasheng He, Chengwei Zhang, Tengfei Bian, et al.
Processes (2023) Vol. 11, Iss. 2, pp. 330-330
Open Access | Times Cited: 30
Advanced bioremediation by an amalgamation of nanotechnology and modern artificial intelligence for efficient restoration of crude petroleum oil-contaminated sites: a prospective study
Rupshikha Patowary, Arundhuti Devi, Ashis K. Mukherjee
Environmental Science and Pollution Research (2023) Vol. 30, Iss. 30, pp. 74459-74484
Open Access | Times Cited: 27
Differences in learning characteristics between support vector machine and random forest models for compound classification revealed by Shapley value analysis
Friederike Maite Siemers, Jürgen Bajorath
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 26
Applications of machine learning in supercritical fluids research
Lucien Roach, Gian‐Marco Rignanese, Arnaud Erriguible, et al.
The Journal of Supercritical Fluids (2023) Vol. 202, pp. 106051-106051
Open Access | Times Cited: 25
Navigating with chemometrics and machine learning in chemistry
Payal B. Joshi
Artificial Intelligence Review (2023) Vol. 56, Iss. 9, pp. 9089-9114
Open Access | Times Cited: 24
Which molecular properties determine the impact sensitivity of an explosive? A machine learning quantitative investigation of nitroaromatic explosives
Júlio César Duarte, Romulo Dias da Rocha, Itamar Borges
Physical Chemistry Chemical Physics (2023) Vol. 25, Iss. 9, pp. 6877-6890
Open Access | Times Cited: 24
A Review of Large Language Models and Autonomous Agents in Chemistry
Mayk Caldas Ramos, Christopher J. Collison, Andrew Dickson White
Chemical Science (2024)
Open Access | Times Cited: 14
Predicting the formation of NADES using a transformer-based model
Lucas B. Ayres, Federico J.V. Gómez, María Fernanda Silva, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 13
Machine learning based hybrid ensemble models for prediction of organic dyes photophysical properties: Absorption wavelengths, emission wavelengths, and quantum yields
Kapil Dev Mahato, Shraban Das, Chandrashekhar Azad, et al.
APL Machine Learning (2024) Vol. 2, Iss. 1
Open Access | Times Cited: 12
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