Read-across predictions of property and ecotoxicity endpoints of selected class of chemicals

P.I.: Prof. Kunal Roy,

Drug Theoretics and Cheminformatics Laboratory,

Jadavpur University, Kolkata-700032

Funded by (2022-2025)

GPC Regulatory India Pvt Ltd, Nagpur, India

Objectives of the Project

1.      To develop and validate read-across and/or QSAR prediction tools for acute and chronic ecotoxicity endpoints of industrial chemicals (daphnia, algae and fish toxicity)

2.       To develop and validate read-across and/or QSPR prediction tools for selected property endpoints of industrial chemicals (bioconcentration, biodegradation, etc.)

3.      To predict ecotoxicity (daphnia, algae and fish toxicity)and property endpoint values of chemicals of interest using read-across and/or QSAR

Work Element 1

Kumar, A., Ojha, P.K. and Roy, K., 2023. QSAR modeling of chronic rat toxicity of diverse organic chemicals. Computational Toxicology, 26, p.100270. https://doi.org/10.1016/j.comtox.2023.100270

Work element 2

Kumar, A., Ojha, P.K. and Roy, K., 2024. Chemometric modeling of the lowest observed effect level (LOEL) and no observed effect level (NOEL) for rat toxicity. Environmental Science: Advances, 3(5), pp.686-705. DOI: 10.1039/D3VA00265A

Work element 3

Kumar, A., Ojha, P.K. and Roy, K., 2024. The first report on the assessment of maximum acceptable daily intake (MADI) of pesticides for humans using intelligent consensus predictions. Environmental Science: Processes & Impacts, 26(5), pp.870-881. DOI: 10.1039/D4EM00059E

Work element 4

Kumar, A., Kumar, V., Ojha, P.K. and Roy, K., 2024. Chronic aquatic toxicity assessment of diverse chemicals on Daphnia magna using QSAR and chemical read-across. Regulatory Toxicology and Pharmacology, 148, p.105572. https://doi.org/10.1016/j.yrtph.2024.105572

Work element 5

Kumar, A., Ojha, P.K. and Roy, K., 2024. First report on pesticide sub-chronic and chronic toxicities against dogs using QSAR and chemical read-across. SAR and QSAR in Environmental Research, 35(3), pp.241-263. https://doi.org/10.1080/1062936X.2024.2320143

Work element 6

Kumar, A., Ojha, P.K. and Roy, K., 2024. Safer and greener chemicals for the aquatic ecosystem: Chemometric modeling of the prolonged and chronic aquatic toxicity of chemicals on Oryzias latipes. Aquatic Toxicology, p.106985. https://doi.org/10.1016/j.aquatox.2024.106985

Work element 7

Kumar, A., Ojha, P.K. and Roy, K., 2024. First report on regression-based QSAR addressing pesticide dissipation half-life in plants: A step towards sustainable public health. Science of The Total Environment, 954, p.176175. https://doi.org/10.1016/j.scitotenv.2024.176175

Work element 8

Kumar, A., Ojha, P.K. and Roy, K., 2025. Chronic and acute eco-toxicity modeling of carcinogenic and hazardous air pollutants toward humans for critical risk assessment and regulatory decision-making. Computational Toxicology, 34, 100358. https://doi.org/10.1016/j.comtox.2025.100358

Work element 9

Pandey S K, Roy K, Quantitative read-across structure-property relationship (q-RASPR)-based lipid-normalized dietary biomagnification (BMFL) prediction: A framework for evaluating biomagnification potential of organic chemicals in the aquatic ecosystem. Aquat Toxicol 286, 2025, 107441, https://doi.org/10.1016/j.aquatox.2025.107441  

Work element 10

Pandey S K, Pore S, Roy K, Assessing biodegradability potential of organic chemicals in aquatic and soil environment through classification-based machine learning models developed in accordance with OECD standards. Sci Total Environ, 1000, 2025, 180376, https://doi.org/10.1016/j.scitotenv.2025.180376

BiodegPred V1.0: A user-friendly Python-based predictive tool that predicts whether a given compound, or a set of compounds, is biodegradable or non-biodegradable in both water and soil environments simultaneously (OECD TG 302 & 307). Additionally, it evaluates the applicability domain (AD) status of the query chemical using the leverage approach for the built-in models. The SMILES notation of the query compound is needed to predict its biodegradability using the BiodegPred tool.

Tool link: https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home/biodegradation-prediction

Work element 11

Pandey S K, Pore S, Roy K, HydroFate - A machine learning-based classification modeling platform for the prediction of hydrolytic stability of organic chemicals across different pH environments. Sci Total Environ 1013, 2026, 181306. https://doi.org/10.1016/j.scitotenv.2025.181306

HydroFate: A Python-based tool that allows users to predict the hydrolytic potential, i.e., whether the given compound is hydrolytically unstable or hydrolytically stable under acidic (pH 4), neutral (pH 7), and basic (pH 9) conditions (OECD TG 111). The tool also provides the AD status for the given compound(s), which signifies whether the prediction made is reliable or not. The tool only needs the SMILES notation of the chemical for which the prediction is to be made.

Tool link: https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home/hydrofate

Acknowledgement: Funding by Global Product Compliance Group 

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Last updated on January 08, 2026