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)

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).

• Integrated Modeling of Organic Chemicals in Tadpole Ecotoxicological Assessment: Exploring Qstr, Q-Rasar, and Intelligent Consensus Prediction Techniques. SSRN (Preprint) 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).

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).

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)