q-RASAR

The novel quantitative read-across structure-activity relationship (q-RASAR) approach clubs the advantages of both QSAR and read-across, thus resulting in enhanced productivity for the same level of chemical information used. This approach utilizes the similarity- based considerations yet can generate simple, interpretable and transferable models. This approach may be used for any type of structural and physicochemical descriptors and with any modeling algorithms.

(Disclaimer: Illustrative figures on this page are taken from the q-RASAR publications of the DTC Laboratory)

Basics of q-RASAR

q-RASAR vs. QSAR and Read-across

RA function and Banerjee-Roy coefficient (gm)

Different scenarios

Similarity considerations

q-RASAR workflow

RASAR Descriptor computation

List of RASAR descriptors

DTC Plot

q-RASAR publications from the DTC Lab

• First report of q-RASAR modeling toward an approach of easy interpretability and efficient transferability, Molecular Diversity 26, 2847–2862 (2022)

• On Some Novel Similarity-Based Functions Used in the ML-Based q‑RASAR Approach for Efficient Quantitative Predictions of Selected Toxicity End Points. Chem. Res. Toxicol. 36, 446−464 (2023) 

• Efficient predictions of cytotoxicity of TiO2-based multi-component nanoparticles using a machine learning-based q-RASAR approach. Nanotoxicology, 17, 78-93 (2023) 

• A machine learning q-RASPR approach for efficient predictions of the specific surface area of perovskites. Molecular Informatics 42:e202200261 (2023)

• Machine-learning-based similarity meets traditional QSAR: “q-RASAR” for the enhancement of the external predictivity and detection of prediction confidence outliers in an hERG toxicity dataset. Chemometrics and Intelligent Laboratory Systems 237, 104829 (2023)

• Predictive Quantitative Read-Across Structure–Property Relationship Modeling of the Retention Time (Log tR) of Pesticide Residues Present in Foods and Vegetables. J Agric Food Chem (2023) 

• Machine Learning - Based q-RASPR Modeling of Power Conversion Efficiency of Organic Dyes in Dye-Sensitized Solar Cells. Sustainable Energy and Fuels (2023)

• “Data fusion” quantitative read-across structure-activity-activity relationships (q-RASAARs) for the prediction of toxicities of binary and ternary antibiotic mixtures toward three bacterial species. J Hazard Mater, 459, 132129 (2023)

• Prediction-Inspired Intelligent Training for the Development of Classification Read-across Structure–Activity Relationship (c-RASAR) Models for Organic Skin Sensitizers: Assessment of Classification Error Rate from Novel Similarity Coefficients. Chem Res Toxicol (2023)

• Machine learning - based q-RASAR modeling to predict acute contact toxicity of binary organic pesticide mixtures in honey bees. J Hazard Mater (2023)

• Read-across-based intelligent learning: development of a global q-RASAR model for the efficient quantitative predictions of skin sensitization potential of diverse organic chemicals. Environ Sci: Process Impact (2023)

• Machine learning-based q-RASPR predictions of detonation heat for nitrogen-containing compounds. Materials Advances (2023)

• Quantitative Read-Across Structure-Activity Relationship (q-RASAR): A New Approach Methodology to Model Aquatic Toxicity of Organic Pesticides Against Different Fish Species. Aquatic Toxicol (2023)

• Development of a read-across-derived classification model for the predictions of mutagenicity data and its comparison with traditional QSAR models and expert systems. Toxicology (2023)

• Machine learning-based q-RASAR approach for the in silico identification of novel multi-target inhibitors against Alzheimer's disease. Chemom Intell Lab Syst (2023) 

• Application of machine learning-based read-across structure-property relationship (RASPR) as a new tool for predictive modelling: Prediction of power conversion efficiency (PCE) for selected classes of organic dyes in dye-sensitized solar cells (DSSCs). Mol Informatics (2024)

• Breaking the Barriers: Machine-Learning-Based c-RASAR Approach for Accurate BloodBrain Barrier Permeability Prediction. J Chem Inf Model (2024)

• Quantitative Read-Across Structure-Activity Relationship (q-RASAR): A novel approach to estimate the subchronic oral safety (NOAEL) of diverse organic chemicals in rats. Toxicology (2024)

• Predicting performance and stability parameters of energetic materials (EMs) using the machine learning-based q-RASPR approach. Energy Advances (2024).

• Innovative Strategies for the Quantitative Modeling of Blood-Brain Barrier (BBB) Permeability: Harnessing the Power of Machine Learning-based q-RASAR Approach. Mol Syst Des Eng (2024)

• How to correctly develop q-RASAR models for predictive cheminformatics. Expert Opin Drug Discov (2024)

• QSPR and q-RASPR predictions of the adsorption capacity of polyethylene, polypropylene and polystyrene microplastics for various organic pollutants in diverse aqueous environments. Environ Sci: Nano (2024)

• 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. Crit Rev Toxicol (2024).

• The application of chemical similarity measures in an unconventional modeling framework c-RASAR along with dimensionality reduction techniques to a representative hepatotoxicity dataset. Scientific Reports 14, 20812 (2024)

• Predictive cheminformatics modeling of reorganization energy (RE) for p-type organic semiconductors: Integration of quantitative read-across structure-property relationship (q-RASPR) and stacking regression analysis. Materials Today Communications (2024).

• Hybrid model development through the integration of quantitative read-across (qRA) hypothesis with the QSAR framework: An alternative risk assessment of acute inhalation toxicity testing in rats. Chemosphere 370, 143931 (2025)

• Prediction of bioconcentration factors (BCFs) and bioaccumulation factors (BAFs) for per- and polyfluoroalkyl substances (PFASs) using Read-Across and q-RASPR. Science of the Total Environment 993, 180007 (2025)

• Exploration of read-across-based predictions for unveiling the risk of FDA-labeled drug-induced cardiotoxicity (DICT) rank: A careful classification of the most extensive reference list of human drugs. In Silico Research Biomed 1, 100039 (2025)

• 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, 180376 (2025)

• A q-RASAR Approach for Oral and Inhalational Toxicity Prediction of Perfluorinated and Polyfluorinated Compounds (PFCs) Using Rodent Toxicity Data. NAM Journal 100061 (2025)

• The multiclass ARKA framework for developing improved q-RASAR models for environmental toxicity endpoints. Environ Sci: Process Impacts 27, 1229-1243 (2025)

• A new approach methodology (NAM) for carcinogenicity prediction of organic chemicals using the multiclass ARKA framework and machine-learning-based stacking regression. J Hazard Mater 496, 139302 (2025) 

• Comparative machine learning and deep learning frameworks for robust carcinogenicity prediction and activity cliff analysis. Environ Sci: Process Impacts (2026)

Publications with q-RASAR modeling from other laboratories

• Ecotoxicological QSAR study of fused/non-fused polycyclic aromatic hydrocarbons (FNFPAHs): Assessment and priority ranking of the acute toxicity to Pimephales promelas by QSAR and consensus modeling methods. Science of The Total Environment, 876, 162736 (2023) 

• In silico assessment of risks associated with pesticides exposure during pregnancy. Chemosphere, 329, 138649 (2023)

• Data driven toxicity assessment of organic chemicals against Gammarus species using QSAR approach. Chemosphere 328, 138433 (2023)

• QSAR and Chemical Read-Across Analysis of 370 Potential MGMT Inactivators to Identify the Structural Features Influencing Inactivation Potency. Pharmaceutics 15, 2170 (2023)

• First report on chemometric modeling of tilapia fish aquatic toxicity to organic chemicals: Toxicity data gap filling. Science of the Total Environment (2023).

• Unveiling first report on in silico modeling of aquatic toxicity of organic chemicals to Labeo rohita (Rohu) employing QSAR and q-RASAR. Chemosphere (2023).

• q-RASTR modelling for prediction of diverse toxic chemicals towards T. pyriformis. SAR QSAR Environ Res (2024).

• Integrated predictive QSAR, Read Across, and q-RASAR analysis for diverse agrochemical phytotoxicity in oat and corn: A consensus-based approach for risk assessment and prioritization. Environ Sci Pollut Res (2024).

• Elucidation of molecular mechanisms involved in tadpole toxicity employing QSTR and q-RASAR approach. Aquatic Toxicoogy 2024.

• In silico soil degradation and ecotoxicity analysis of veterinary pharmaceuticals on terrestrial species: first report. Toxicol Res 13, tfae020 (2024) 

• Chemometrics-driven prediction and prioritization of diverse pesticides on chickens for addressing hazardous effects on public health. J Hazard Mater 134326 (2024).

• Prediction of acute toxicity for Chlorella vulgaris caused by tire wear particle-derived compounds using quantitative structure-activity relationship models. Water Research (2024).

•  Unveiling critical structural features for effective HDAC8 inhibition: a comprehensive study using quantitative read-across structure–activity relationship (q-RASAR) and pharmacophore modeling. Mol. Divers. (2024)

• From molecular descriptors to the developmental toxicity prediction of pesticides/veterinary drugs/bio-pesticides against zebrafish embryo: dual computational toxicological approaches for prioritization. J Hazard Mater (2024).

• How Safe Are Wild-Caught Salmons Exposed to Various Industrial Chemicals? First Ever In Silico Models for Salmon Toxicity Data Gaps Filling. J Hazard Mater (2024)

• Accurate Prediction of Rat Acute Oral Toxicity and Reference Dose for Thousands of Polycyclic Aromatic Hydrocarbon Derivatives Based on Chemometric QSAR and Machine Learning. Environ Sci Tech (2024)

• Leveraging New Approach Methodologies: Ecotoxicological Modelling of Endocrine Disrupting Chemicals to Danio rerio through Machine Learning and Toxicity Studies. Toxicol Mech Methods (2024)

• Introducing third-generation periodic table descriptors for nano-qRASTR modeling of zebrafish toxicity of metal oxide nanoparticles. Beilstein J Nanotech (2024)

• Comparative QSAR and q-RASAR Modeling for Aquatic Toxicity of Organic Chemicals to Three Trout Species: O. Clarkii, S. Namaycush, and S. Fontinalis. J Hazard Mater (2024)

• The oral acute toxicity of per- and polyfluoroalkyl compounds (PFASs) to Rat and Mouse: a mechanistic interpretation and prioritization analysis of untested PFASs by QSAR, q-RASAR and interspecies modelling methods. J Hazard Mater (2024)

• Quantitative read-across structure–property relationship (q-RASPR): a novel approach to estimate the bioaccumulative potential for diverse classes of industrial chemicals in aquatic organisms. Environ Sci: Process Impacts (2024)

• ML‐based Read‐Across Structure‐Property Relationship (RASPR) strategy for Predicting Protein Resistance of Self-Assembled Monolayers (SAMs) as anti-biofouling materials. Materials Today Commun (2024)

• High-throughput prediction of oral acute toxicity in Rat and Mouse of over 100,000 polychlorinated persistent organic pollutants (PC-POPs) by interpretable data fusion-driven machine learning global models. J Hazard Mater 480, 136295 (2024)

• Integrating traditional QSAR and read-across-based regression models for predicting potential anti-leishmanial azole compounds. Mol Divers (2024)

• Decoding cyanide toxicity: Integrating Quantitative Structure-Toxicity Relationships (QSTR) with species sensitivity distributions and q-RASTR modeling. Ecotoxicology and Environmental Safety, 291 (2025) 117824

• First report on q-RASTR modelling of hazardous dose (HD5) for acute toxicity of pesticides: an efficient and reliable approach towards safeguarding the sensitive avian species. SAR QSAR Environ Res (2025)

• Exploring QSTR and q-RASTR modeling of agrochemical toxicity on cabbage for environmental safety and human health. Environ Sci Pollut Res (2025)

• Risk Assessment of Industrial Chemicals Towards Salmon Species Amalgamating QSAR, q-RASAR, and ARKA Framework. Toxicol Reports (2025)

• Modelling of intrinsic membrane permeability of drug molecules by explainable ML-based q-RASPR approach towards better pharmacokinetics and toxicokinetics properties. SAR QSAR Environ Res 36 (2025) 127-143

• Metal bioaccumulation prediction via QSPR-q-RASPR synergy and cross-species risk analysis. Green Chem (2025)

• Assessment of the rat acute oral toxicity of quinoline-based pharmaceutical scaffold molecules using QSTR, q-RASTR and machine learning methods. Mol Divers (2025)

• Revolutionizing toxicity predictions of diverse chemicals to protect human health: Comparative QSAR and q-RASAR modeling. Toxicol Lett (2025)

• Bridging chemistry and toxicology: Chemometric-driven prediction and prioritization of various drugs addressing hazardous effects for human safety. J Hazard Mater (2025)

• Machine learning-assisted comparative QSTR, i-QSTTR, qRASTR, and i-qRASTTR modelling for toxicity of Ionic liquids against three different bacteria S. aureus, E. coli, and P. aeruginosa. J Hazard Mater, 139533 (2025)

• From structure to strategy: chemometric modeling for the prediction of terminal half-life of pharmaceuticals and its role in future therapeutics. Mol Divers (2025)

• Integrative QSAR and q-RASAR Models for Danio rerio Acute Toxicity from Short-Term Exposures: Mechanistic Insights and Data Gap Filling. J Hazard Mater (2025)

• QSAR, q-RASAR and intelligent consensus-based predictions of agrochemicals for Tomato (Solanum lycopersicum) phytotoxicity: risk assessment, food safety and sustainability.  Toxicology and Environmental Health Sciences (2025)

• Unveiling chemical space, scaffold diversity, critical structural features of pesticides: A comprehensive QSAR, qRASAR, machine learning studies to predict pesticides toxicity. Sci Total Environ, 1001, 180489 (2025)

• Machine learning-guided Nano-QSAR modeling predicts HepaRG cell membrane toxicity of engineered nanoparticles with mechanistic insights. Cell Biol Toxicol (2026)

• Predictive q-RASAR modeling of intrinsic toxicity of pharmaceuticals and bioactive compounds in cats. J Hazard Mater 504, 141274 (2026)

• Read-Across Structure-Property Relationship-Based Superior Prediction of Fraction Unbound in Plasma from Chemical Structure: Interpretable Models with Minimum Descriptors. Mol inform 45 (2), e70023 (2026)

• Chemometric modelling for eco-toxicological risk assessment of diverse chemicals towards mammalian species. Chemosphere 397, 144877 (2026)

• KOC-WebPredictor: An Open-Access Tool for Prediction and Insights into Soil Sorption. ACS Omega (2026)

Publications on q-RASAR from the DTC Lab in collaboration with other Laboratories

• Organic Sunscreens and Their Products of Degradation in Biotic and Abiotic Conditions—In Silico Studies of Drug-Likeness and Human Placental Transport. Int. J. Mol. Sci. 25(22), 12373 (2024)

• The round-robin approach applied to nanoinformatics: consensus prediction of nanomaterials zeta potential. Beilstein J. Nanotechnol. 15, 1536–1553 (2024)

Publications on quantitative Read-Across from other laboratories

• Prioritization of the ecotoxicological hazard of PAHs towards aquatic species spanning three trophic levels using 2D-QSTR, read-across and machine learning-driven modelling approaches. J Hazard Mater 465, 133410 (2024).

• The rat acute oral toxicity of trifluoromethyl compounds (TFMs): a computational toxicology study combining the 2D-QSTR, read-across and consensus modeling methods. Archives of Toxicology (2024).

• Comprehensive Ecotoxicological Assessment of Pesticides on Multiple Avian Species: Employing Quantitative Structure-Toxicity Relationship (QSTR) Modeling and Read-Across. Process Saf Environ Protect (2024).

• New binary mixtures of fungicides against Macrophomina phaseolina: machine learning-driven QSAR, read-across prediction, and molecular dynamics simulation. Chemosphere (2024)

• First report on retention time prediction of pesticides and veterinary drugs in cow milk using read-across and intelligent consensus prediction: an alternative for hazard assessment employing food-informatics. SAR QSAR Environ Res 36, 421-441 (2025)

• Exploring aquatic toxicity of diverse pesticides against Chironomus riparius (harlequin fly) and Lemna gibba (swollen duckweed): Applications of QSTR, i-QSTTR, and novel q-RA approaches. Sci Total Environment 1000, 180384 (2025)

Publications on quantitative Read-Across from the DTC Lab

• A novel quantitative read-across tool designed purposefully to fill the existing gaps in nanosafety data. Environ Sci Nano 9, 189-203 (2022)

• Quantitative predictions from chemical read-across and their confidence measures. Chemom Intell Lab Syst 227, 104613 (2022)

• Quick and efficient quantitative predictions of androgen receptor binding affinity for screening Endocrine Disruptor Chemicals using 2D-QSAR and Chemical Read-Across. Chemosphere 309, 136579 (2022).

• Nano-read-across predictions of toxicity of metal oxide engineered nanoparticles (MeOx ENPS) used in nanopesticides to BEAS-2B and RAW 264.7 cells. Nanotoxicology 16, 629-644 (2022)

• First report on soil ecotoxicity prediction against Folsomia candida using intelligent consensus predictions and chemical read-across. Environ Sci Pollut Res 29, 88302–88317 (2022)

• Computational modeling of aquatic toxicity of polychlorinated naphthalenes (PCNs) employing 2D-QSAR and chemical read-across. Aquatic Toxicology 257, 106429 (2023).

• Chronic aquatic toxicity assessment of diverse chemicals on Daphnia magna using QSAR and chemical read-across. Regulat Toxicol Pharmacol 148, 105572 (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), 241-263 (2024)

• 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. Toxicol in Vitro 106018 (2025)

RASAR Tools 

RASAR Descriptor Calculator

Auto RA optimizer

Tools developed by Arkaprava Banerjee

RASAR Presentations

Presentation 1 - General - Kunal Roy (BCADD2023)

Presentation 2- Case study - Arkaprava Banerjee (BCADD2023)

q-RA presentations

Presentation (Case studies) - Kunal Roy (BIC Workshop, PU, 2022)

ARKA-RASAR

Presentation - BCADD 2025