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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:

Machine Learning Models for Predicting Molecular UV–Vis Spectra with Quantum Mechanical Properties
Andrew McNaughton, Rajendra P. Joshi, Carter Knutson, et al.
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 5, pp. 1462-1471
Open Access | Times Cited: 28

Showing 1-25 of 28 citing articles:

Chemprop: A Machine Learning Package for Chemical Property Prediction
Esther Heid, Kevin P. Greenman, Yunsie Chung, et al.
Journal of Chemical Information and Modeling (2023) Vol. 64, Iss. 1, pp. 9-17
Open Access | Times Cited: 140
Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy
Xinyuan Bi, Li Lin, Zhou Chen, et al.
Small Methods (2023) Vol. 8, Iss. 1
Closed Access | Times Cited: 40
AI in analytical chemistry: Advancements, challenges, and future directions
Rafael Cardoso Rial
Talanta (2024) Vol. 274, pp. 125949-125949
Closed Access | Times Cited: 21
Accurate Machine Learning for Predicting the Viscosities of Deep Eutectic Solvents
Mood Mohan, Karuna Devi Jetti, Micholas Dean Smith, et al.
Journal of Chemical Theory and Computation (2024) Vol. 20, Iss. 9, pp. 3911-3926
Closed Access | Times Cited: 20
Extrapolative prediction of small-data molecular property using quantum mechanics-assisted machine learning
Hajime Shimakawa, Akiko Kumada, Masahiro Sato
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 16
Open-Source Machine Learning in Computational Chemistry
Alexander Hagg, Karl N. Kirschner
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 15, pp. 4505-4532
Open Access | Times Cited: 26
Advances in the Application of Artificial Intelligence-Based Spectral Data Interpretation: A Perspective
Xi Xue, Hanyu Sun, Minjian Yang, et al.
Analytical Chemistry (2023) Vol. 95, Iss. 37, pp. 13733-13745
Closed Access | Times Cited: 17
Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview
André Nicolle, Sili Deng, Matthias Ihme, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 3, pp. 597-620
Closed Access | Times Cited: 6
High-Throughput Screening and Accurate Prediction of Ionic Liquid Viscosities Using Interpretable Machine Learning
Mood Mohan, Karuna Devi Jetti, Sreelekha Guggilam, et al.
ACS Sustainable Chemistry & Engineering (2024) Vol. 12, Iss. 18, pp. 7040-7054
Closed Access | Times Cited: 6
Prediction of Pt, Ir, Ru, and Rh complexes light absorption in the therapeutic window for phototherapy using machine learning
Vincenzo Vigna, Tânia Cova, Alberto A. C. C. Pais, et al.
Journal of Cheminformatics (2025) Vol. 17, Iss. 1
Open Access
Optimized Machine learning techniques Enable prediction of organic dyes photophysical Properties: Absorption Wavelengths, emission Wavelengths, and quantum yields
Kapil Dev Mahato, Uday Kumar
Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (2023) Vol. 308, pp. 123768-123768
Closed Access | Times Cited: 13
When Metal Nanoclusters Meet Smart Synthesis
Zhucheng Yang, Anye Shi, Ruixuan Zhang, et al.
ACS Nano (2024)
Closed Access | Times Cited: 3
Chemprop: A Machine Learning Package for Chemical Property Prediction
Esther Heid, Kevin P. Greenman, Yunsie Chung, et al.
(2023)
Open Access | Times Cited: 9
Large Property Models: A New Generative Paradigm for Molecules
Tianfan Jin, Veerupaksh Singla, Hsuan‐Hao Hsu, et al.
(2024)
Open Access | Times Cited: 1
Data-driven machine learning models for predicting the maximum absorption and emission wavelengths of single benzene fluorophores
Y.D. Jin, Z.-Y. Wang, Miao Dong, et al.
Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (2024) Vol. 326, pp. 125213-125213
Closed Access | Times Cited: 1
Large property models: a new generative machine-learning formulation for molecules
Tianfan Jin, Veerupaksh Singla, Hsuan‐Hao Hsu, et al.
Faraday Discussions (2024)
Open Access | Times Cited: 1
Investigation of Dual Inhibition of Antibacterial and Antiarthritic Drug Candidates Using Combined Approach Including Molecular Dynamics, Docking and Quantum Chemical Methods
Shabbir Muhammad, Amina Faiz, Shamsa Bibi, et al.
Computational Biology and Chemistry (2024) Vol. 113, pp. 108218-108218
Closed Access | Times Cited: 1
Prediction of the Infrared Absorbance Intensities and Frequencies of Hydrocarbons: A Message Passing Neural Network Approach
Maliheh Shaban Tameh, Veaceslav Coropceanu, Thomas A. R. Purcell, et al.
The Journal of Physical Chemistry A (2024) Vol. 128, Iss. 44, pp. 9695-9706
Open Access | Times Cited: 1
Physics-informed machine learning to predict solvatochromic parameters of designer solvents with case studies in CO2 and lignin dissolution
Mood Mohan, Nikhitha Gugulothu, Sreelekha Guggilam, et al.
Green Chemical Engineering (2024)
Open Access | Times Cited: 1
Machine learning prediction of organic moieties from the IR spectra, enhanced by additionally using the derivative IR data
Maurycy Krzyżanowski, Grzegorz Matyszczak
Chemical Papers (2024) Vol. 78, Iss. 5, pp. 3149-3173
Open Access
Prediction of structure based on spectral data using computational techniques
Fyaz M.D. Ismail, Lutfun Nahar, Satyajit D. Sarker
Elsevier eBooks (2024), pp. 255-300
Closed Access
The golden age of colorimetry in diagnostics and drug analysis: Focus on the rising star of metabolite-based assays
Pasquale Palladino, Mariagrazia Lettieri, Simona Scarano, et al.
TrAC Trends in Analytical Chemistry (2024) Vol. 180, pp. 117947-117947
Closed Access
A Machine Learning Approach for Efficiently Predicting Polymer Aging from UV–Vis Spectra
Haishan Yu, DaDi Zhang, Lei Cui, et al.
The Journal of Physical Chemistry B (2024)
Closed Access
AGAPE (computAtional G-quadruplex Affinitiy PrEdiction): The first AI In-silico workflow for G-quadruplex binding affinity prediction.
Luisa D’Anna, Ugo Perricone, Giada De Simone, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Deep learning of spectra: Predicting the dielectric function of semiconductors
Malte Grunert, Max Großmann, Erich Runge
Physical Review Materials (2024) Vol. 8, Iss. 12
Open Access
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