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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:

Binary and multi-class classification for androgen receptor agonists, antagonists and binders
Geven Piir, Sulev Sild, Uko Maran
Chemosphere (2020) Vol. 262, pp. 128313-128313
Closed Access | Times Cited: 28

Showing 1-25 of 28 citing articles:

Effect of Dataset Size and Train/Test Split Ratios in QSAR/QSPR Multiclass Classification
Anita Rácz, Dávid Bajusz, Károly Héberger
Molecules (2021) Vol. 26, Iss. 4, pp. 1111-1111
Open Access | Times Cited: 215
First report of q-RASAR modeling toward an approach of easy interpretability and efficient transferability
Arkaprava Banerjee, Kunal Roy
Molecular Diversity (2022) Vol. 26, Iss. 5, pp. 2847-2862
Open Access | Times Cited: 98
Explainable AI-driven prediction of APE1 inhibitors: enhancing cancer therapy with machine learning models and feature importance analysis
Aga Basit Iqbal, Tariq Masoodi, Ajaz A. Bhat, et al.
Molecular Diversity (2025)
Closed Access | Times Cited: 1
Quick and efficient quantitative predictions of androgen receptor binding affinity for screening Endocrine Disruptor Chemicals using 2D-QSAR and Chemical Read-Across
Arkaprava Banerjee, Priyanka De, Vinay Kumar, et al.
Chemosphere (2022) Vol. 309, pp. 136579-136579
Open Access | Times Cited: 38
Remediation and toxicity of endocrine disruptors: a review
Ravichandran Swathy Monisha, Ragupathy Lakshmi Mani, Baskaran Sivaprakash, et al.
Environmental Chemistry Letters (2022) Vol. 21, Iss. 2, pp. 1117-1139
Closed Access | Times Cited: 29
Cheminformatic Analysis and Machine Learning Modeling to Investigate Androgen Receptor Antagonists to Combat Prostate Cancer
Tianshi Yu, Chanin Nantasenamat, Supicha Kachenton, et al.
ACS Omega (2023) Vol. 8, Iss. 7, pp. 6729-6742
Open Access | Times Cited: 13
Rethinking the applicability domain analysis in QSAR models
José R. Mora, Edgar Márquez, Noel Pérez, et al.
Journal of Computer-Aided Molecular Design (2024) Vol. 38, Iss. 1
Closed Access | Times Cited: 5
Predictive Modeling of Pesticides Reproductive Toxicity in Earthworms Using Interpretable Machine-Learning Techniques on Imbalanced Data
Mihkel Kotli, Geven Piir, Uko Maran
ACS Omega (2025)
Open Access
A machine learning model for classifying G-protein-coupled receptors as agonists or antagonists
Jooseong Oh, Hyi-thaek Ceong, Dokyun Na, et al.
BMC Bioinformatics (2022) Vol. 23, Iss. S9
Open Access | Times Cited: 17
Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases
Christophe Müller, Obdulia Rabal, Constantino Diaz Gonzalez
Methods in molecular biology (2021), pp. 383-407
Closed Access | Times Cited: 22
Quantitative structure activity relationship studies of androgen receptor binding affinity of endocrine disruptor chemicals with index of ideality of correlation, their molecular docking, molecular dynamics and ADME studies
Surbhi Goyal, Payal Rani, Monika Chahar, et al.
Journal of Biomolecular Structure and Dynamics (2023) Vol. 41, Iss. 23, pp. 13616-13631
Closed Access | Times Cited: 8
Predicting the binding of small molecules to nuclear receptors using machine learning
Azhagiya Singam Ettayapuram Ramaprasad, Martyn T. Smith, David McCoy, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 3
Open Access | Times Cited: 14
Machine Learning Quantitative Structure–Property Relationships as a Function of Ionic Liquid Cations for the Gas-Ionic Liquid Partition Coefficient of Hydrocarbons
Karl Marti Toots, Sulev Sild, Jaan Leis, et al.
International Journal of Molecular Sciences (2022) Vol. 23, Iss. 14, pp. 7534-7534
Open Access | Times Cited: 13
The quantitative structure-property relationships for the gas-ionic liquid partition coefficient of a large variety of organic compounds in three ionic liquids
Karl Marti Toots, Sulev Sild, Jaan Leis, et al.
Journal of Molecular Liquids (2021) Vol. 343, pp. 117573-117573
Closed Access | Times Cited: 13
Pesticide effect on earthworm lethality via interpretable machine learning
Mihkel Kotli, Geven Piir, Uko Maran
Journal of Hazardous Materials (2023) Vol. 461, pp. 132577-132577
Closed Access | Times Cited: 5
A quantitative Heppes theorem and multivariate Bernoulli distributions
Ricardo Fraiman, Leonardo Moreno, Thomas Ransford
Journal of the Royal Statistical Society Series B (Statistical Methodology) (2023) Vol. 85, Iss. 2, pp. 293-314
Open Access | Times Cited: 4
Exploring the Influence of Ionic Liquid Anion Structure on Gas-Ionic Liquid Partition Coefficients of Organic Solutes Using Machine Learning
Karl Marti Toots, Sulev Sild, Jaan Leis, et al.
Langmuir (2024) Vol. 40, Iss. 45, pp. 23714-23728
Open Access | Times Cited: 1
Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy
Dongxue Zhao, Xin Wang, Yashuang Mu, et al.
Entropy (2021) Vol. 23, Iss. 7, pp. 822-822
Open Access | Times Cited: 10
Read-Across and RASAR Tools from the DTC Laboratory
Arkaprava Banerjee, Kunal Roy
Challenges and advances in computational chemistry and physics (2023), pp. 239-268
Closed Access | Times Cited: 3
Integrating molecular descriptors for enhanced prediction: Shedding light on the potential of pH to model hydrated electron reaction rates for organic compounds
Yi Li, Cuicui Tao, Dafang Fu, et al.
Chemosphere (2023) Vol. 349, pp. 140984-140984
Closed Access | Times Cited: 3
Combined Naïve Bayesian, Chemical Fingerprints and Molecular Docking Classifiers to Model and Predict Androgen Receptor Binding Data for Environmentally- and Health-Sensitive Substances
Alfonso T. García‐Sosa, Uko Maran
International Journal of Molecular Sciences (2021) Vol. 22, Iss. 13, pp. 6695-6695
Open Access | Times Cited: 5
Tools, Applications, and Case Studies (q-RA and q-RASAR)
Kunal Roy, Arkaprava Banerjee
Springer briefs in molecular science (2024), pp. 51-88
Closed Access
Identification of estrogen receptor agonists among hydroxylated polychlorinated biphenyls using classification-based quantitative structure–activity relationship models
Lukman K. Akinola, Adamu Uzairu, Gideon Adamu Shallangwa, et al.
Current Research in Toxicology (2024) Vol. 6, pp. 100158-100158
Open Access
QSPRmodeler - An open source application for molecular predictive analytics
Rafał A. Bachorz, Damian Nowak, Marcin Ratajewski
Frontiers in Bioinformatics (2024) Vol. 4
Open Access
Interpretable machine learning for the identification of estrogen receptor agonists, antagonists, and binders
Geven Piir, Sulev Sild, Uko Maran
Chemosphere (2023) Vol. 347, pp. 140671-140671
Closed Access | Times Cited: 1
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