Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

ARKA: a framework of dimensionality reduction for machine-learning classification modeling, risk assessment, and data gap-filling of sparse environmental toxicity data
Arkaprava Banerjee, Kunal Roy
Environmental Science Processes & Impacts (2024) Vol. 26, Iss. 6, pp. 991-1007
Open Access | Times Cited: 25

Showing 25 citing articles:

Molecular similarity in chemical informatics and predictive toxicity modeling: from quantitative read-across (q-RA) to quantitative read-across structure–activity relationship (q-RASAR) with the application of machine learning
Arkaprava Banerjee, Supratik Kar, Kunal Roy, et al.
Critical Reviews in Toxicology (2024) Vol. 54, Iss. 9, pp. 659-684
Closed Access | Times Cited: 10
Integrating Machine Learning and Nano-QSAR Models to Predict the Oxidative Stress Potential Caused by Single and Mixed Carbon Nanomaterials in Algal Cells
Qi Qi, Zhuang Wang
Environmental Toxicology and Chemistry (2025)
Closed Access | Times Cited: 1
Predictive classification-based read-across for diverse functional vitiligo-linked chemical exposomes (ViCE): A new approach for the assessment of chemical safety for the vitiligo disease in humans
Shilpayan Ghosh, Sapna Kumari Pandey, Kunal Roy
Toxicology in Vitro (2025) Vol. 104, pp. 106018-106018
Closed Access
Web Server-based Deep Learning-Driven Predictive Models for Respiratory Toxicity of Environmental Chemicals: Mechanistic Insights and Interpretability
Na Li, Zhaoyang Chen, Wenhui Zhang, et al.
Journal of Hazardous Materials (2025) Vol. 489, pp. 137575-137575
Closed Access
PBScreen: A Server for the High-Throughput Screening of Placental Barrier–Permeable Contaminants Based on Multifusion Deep Learning
Yuchen Gao, Yu Qiu, Fang Wan, et al.
Environmental Pollution (2025), pp. 125858-125858
Closed Access
Explainable machine learning models enhance prediction of PFAS bioactivity using quantitative molecular surface analysis-derived representation
Zhipeng Yin, Min Zhang, Runzeng Liu, et al.
Water Research (2025), pp. 123500-123500
Closed Access
Modeling and Interpretability Study of the Structure–Activity Relationship for Multigeneration EGFR Inhibitors
Zhiqi Sun, Donghui Huo, Jiangyu Guo, et al.
ACS Omega (2025)
Open Access
Introduction to Machine Learning for Predictive Modeling I
Zhaoyang Chen, Na Li, Xiao Li
Challenges and advances in computational chemistry and physics (2025), pp. 3-30
Closed Access
Risk Assessment of Industrial Chemicals Towards Salmon Species Amalgamating QSAR, q-RASAR, and ARKA Framework
P.K. Bhattacharyya, Shubha Das, Probir Kumar Ojha
Toxicology Reports (2025), pp. 102017-102017
Open Access
The application of chemical similarity measures in an unconventional modeling framework c-RASAR along with dimensionality reduction techniques to a representative hepatotoxicity dataset
Arkaprava Banerjee, Kunal Roy
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 4
Machine learning assisted classification RASAR modeling for the nephrotoxicity potential of a curated set of orally active drugs
Arkaprava Banerjee, Kunal Roy
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Innovative Strategies for the Quantitative Modeling of Blood-Brain Barrier (BBB) Permeability: Harnessing the Power of Machine Learning-based q-RASAR Approach
Vinay Kumar, Arkaprava Banerjee, Kunal Roy
Molecular Systems Design & Engineering (2024) Vol. 9, Iss. 7, pp. 729-743
Closed Access | Times Cited: 3
Explainable machine learning models for predicting the acute toxicity of pesticides to sheepshead minnow (Cyprinodon variegatus)
Ting Sun, Chenxi Wei, Yang Liu, et al.
The Science of The Total Environment (2024) Vol. 957, pp. 177399-177399
Closed Access | Times Cited: 2
Tensile strength prediction of steel sheets: An insight into data-driven models, dimensionality reduction, and feature importance
Gerfried Millner, Manfred Mücke, Lorenz Romaner, et al.
Modelling and Simulation in Materials Science and Engineering (2024) Vol. 32, Iss. 7, pp. 075004-075004
Open Access | Times Cited: 1
Contributions to the development of prediction models for the toxicity of ionic liquids
Hayet Abdellatif, Maamar Laidi, Cherif Si‐Moussa, et al.
Structural Chemistry (2024)
Closed Access | Times Cited: 1
Organic Sunscreens and Their Products of Degradation in Biotic and Abiotic Conditions—In Silico Studies of Drug-Likeness and Human Placental Transport
Anna W. Sobańska, Arkaprava Banerjee, Kunal Roy
International Journal of Molecular Sciences (2024) Vol. 25, Iss. 22, pp. 12373-12373
Open Access | Times Cited: 1
Evaluating ionic liquid toxicity with machine learning and structural similarity methods
Rongli Shan, Runqi Zhang, Ying Gao, et al.
Green Chemical Engineering (2024)
Open Access
Leveraging New Approach Methodologies: Ecotoxicological Modelling of Endocrine Disrupting Chemicals to Danio rerio through Machine Learning and Toxicity Studies
Gopal Italiya, Sangeetha Subramanian
Toxicology Mechanisms and Methods (2024), pp. 1-17
Closed Access
Innovative molecular descriptors in QSPR modeling: Integrating Carnahan-Starling EoS for predicting diffusion coefficients in hydrocarbons and mixtures
Duo Li, Fubin Yang, Xin Wang, et al.
Journal of Molecular Liquids (2024), pp. 125994-125994
Closed Access
Development of a robust Machine learning model for Ames test outcome prediction
Gori Sankar Borah, Selvaraman Nagamani
Chemical Physics Letters (2024), pp. 141663-141663
Closed Access
Accurate forecasting of bioconcentration factor by incorporating quantum chemical method in the QSAR model
Xiaojie Feng, Jialiang Xiong, Xiao Liu, et al.
Journal of Water Process Engineering (2024) Vol. 68, pp. 106482-106482
Closed Access
Unveiling the interspecies correlation and sensitivity factor analysis of rat and mouse acute oral toxicity of antimicrobial agents: first QSTR and QTTR Modeling report
Purusottam Banjare, Anjali Murmu, Balaji Wamanrao Matore, et al.
Toxicology Research (2024) Vol. 13, Iss. 6
Closed Access
A comprehensive machine learning-based models for predicting mixture toxicity of azole fungicides toward algae (Auxenochlorella pyrenoidosa)
Li‐Tang Qin, Xue-Fang Tian, Jun-Yao Zhang, et al.
Environment International (2024) Vol. 194, pp. 109162-109162
Open Access
Machine learning models for quantitatively prediction of toxicity in macrophages induced by metal oxide nanoparticles
Tianqin Wang, Yang Huang, Hongwu Zhang, et al.
Chemosphere (2024) Vol. 370, pp. 143923-143923
Closed Access
QSAR study on molecular design and flotation prediction of collectors for copper sulfide
Wei Xiong, Wenfei Wang, Hong Zhong, et al.
Minerals Engineering (2024) Vol. 222, pp. 109152-109152
Closed Access
Page 1

Scroll to top