| A novel quantitative read-across tool designed purposefully to fill the existing gaps in nanosafety data M Chatterjee, A Banerjee, P De, A Gajewicz-Skretna, K Roy Environmental Science: Nano 9 (1), 189-203, 2022 | 61 | 2022 |
| First report of q-RASAR modeling towards an approach of easy interpretability and efficient transferability A Banerjee, K Roy Molecular Diversity 26, 2847-2862, 2022 | 60 | 2022 |
| Quantitative Predictions from Chemical Read-Across and Their Confidence Measures A Banerjee, M Chatterjee, P De, K Roy Chemometrics and Intelligent Laboratory Systems 227, 104613, 2022 | 39 | 2022 |
| Quick and Efficient Quantitative Predictions of Androgen Receptor Binding Affinity for Screening Endocrine Disruptor Chemicals Using 2D-QSAR and Chemical Read-Across A Banerjee, P De, V Kumar, S Kar, K Roy Chemosphere 309, 136579, 2022 | 28 | 2022 |
| On some novel similarity-based functions used in the ML-based q-RASAR approach for efficient quantitative predictions of selected toxicity end points A Banerjee, K Roy Chemical Research in Toxicology 36 (3), 446-464, 2023 | 27 | 2023 |
| Prediction-inspired intelligent training for the development of classification read-across structure–activity relationship (c-RASAR) models for organic skin sensitizers … A Banerjee, K Roy Chemical Research in Toxicology 36 (9), 1518-1531, 2023 | 20 | 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 … A Banerjee, K Roy Chemometrics and Intelligent Laboratory Systems 237, 104829, 2023 | 19 | 2023 |
| Efficient predictions of cytotoxicity of TiO2-based multi-component nanoparticles using a machine learning-based q-RASAR approach A Banerjee, S Kar, S Pore, K Roy Nanotoxicology 17 (1), 78-93, 2023 | 17 | 2023 |
| A machine learning q‐RASPR approach for efficient predictions of the specific surface area of perovskites A Banerjee, A Gajewicz‐Skretna, K Roy Molecular Informatics 42 (4), 2200261, 2023 | 15 | 2023 |
| Machine learning-based q-RASAR modeling to predict acute contact toxicity of binary organic pesticide mixtures in honey bees M Chatterjee, A Banerjee, S Tosi, E Carnesecchi, E Benfenati, K Roy Journal of Hazardous Materials 460, 132358, 2023 | 10 | 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 … A Banerjee, K Roy Environmental Science: Processes & Impacts 25 (10), 1626-1644, 2023 | 7 | 2023 |
| Machine learning-based q-RASPR modeling of power conversion efficiency of organic dyes in dye-sensitized solar cells S Pore, A Banerjee, K Roy Sustainable Energy & Fuels 7 (14), 3412-3431, 2023 | 5 | 2023 |
| Machine learning-based q-RASAR approach for the in silico identification of novel multi-target inhibitors against Alzheimer's disease V Kumar, A Banerjee, K Roy Chemometrics and Intelligent Laboratory Systems 245, 105049, 2024 | 2 | 2024 |
| Prediction-inspired intelligent training for the development of c-RASAR models for organic skin sensitizers: Assessment of classification error rate from novel similarity … A Banerjee, K Roy | 2 | 2023 |
| Read-across and RASAR tools from the DTC laboratory A Banerjee, K Roy Current Trends in Computational Modeling for Drug Discovery, 239-268, 2023 | 1 | 2023 |
| Machine learning-based q-RASPR predictions of detonation heat for nitrogen-containing compounds SK Pandey, A Banerjee, K Roy Materials Advances 4 (22), 5797-5807, 2023 | 1 | 2023 |
| # 46 Assessment of androgen receptor binding affinity of endocrine disruptors: A 2D-QSAR approach A Banerjee, P De, K Roy Journal of Pharmaceutical Chemistry 8 (Supplement), 2022 | 1 | 2022 |
| ARKA: A framework of dimensionality reduction for machine-learning classification modeling, risk assessment, and data gap-filling of sparse environmental toxicity data A Banerjee, K Roy | | 2024 |
| Application of machine learning‐based read‐across structure‐property relationship (RASPR) as a new tool for predictive modelling: Prediction of power conversion efficiency (PCE … S Pore, A Banerjee, K Roy Molecular Informatics, e202300210, 2024 | | 2024 |
| Read-Across and Quantitative Structure–Activity Relationships (QSAR) for Making Predictions and Data Gap-Filling K Roy, A Banerjee q-RASAR: A Path to Predictive Cheminformatics, 15-29, 2024 | | 2024 |
KUNAL ROYProfessor, Jadavpur University, DTC Laboratory, Dept. Pharmaceutical Technology, IndiaVerified email at jadavpuruniversity.in
