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Arkaprava Banerjee

Jadavpur UniversityIN
ORCID0000-0001-8468-0784
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18

via OpenAlex

Citations
1,127

via OpenAlex

Publications
51

About

Elsevier Top 2% scientist 2025 (Field: Toxicology) Member of the Royal Society of Chemistry, Member of the American Chemical Society Recipient of Prof. Anupam Sengupta Memorial Bronze Medal for securing the highest marks in Pharmaceutical Chemistry in M.Pharm. Working in the field of QSAR, Cheminformatics, Read-Across, q-RASAR, and the applications of Machine Learning and Deep Learning

Research areas

  • Quantitative structure–activity relationship
  • Artificial intelligence
  • Machine learning
  • Computer science
  • Data mining

Publications (51)

Sorted by most cited.

  1. On Some Novel Similarity-Based Functions Used in the ML-Based q-RASAR Approach for Efficient Quantitative Predictions of Selected Toxicity End Points

    Chemical Research in Toxicology1970journal article

    View DOI
    67 cites

  2. 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)

    Molecular Informatics2024journal article

    View DOI
    18 cites

  3. Machine learning assisted classification RASAR modeling for the nephrotoxicity potential of a curated set of orally active drugs

    Scientific Reports2025journal article

    View DOI
    7 cites

  4. The multiclass ARKA framework for developing improved quantitative RASAR models

    2025

    View DOI
    1 cites

  5. Machine learning-assisted c-RASAR modeling of a curated set of orally active nephrotoxic drugs: Similarity-based predictions from close source neighbors

    2024

    View DOI
    1 cites

  6. From chemical similarity measures to an unconventional modeling framework: The application of c-RASAR along with dimensionality reduction techniques in a representative hepatotoxicity dataset

    2024

    View DOI
    1 cites

  7. Prediction-inspired intelligent training for the development of c-RASAR models for organic skin sensitizers: Assessment of classification error rate from novel similarity coefficients

    ChemRxiv1970other

    View DOI
    1 cites

  8. Machine Learning-Based QSPR Modeling of log CMC Values of Per- and Polyfluoroalkyl Substances (PFASs) and the Identification of Property Cliffs

    Industrial & Engineering Chemistry Research2026journal article

    View DOI
    0 cites

  9. From Feature‐Based Chemical Similarity to Chemical Language Models—A Paradigm Shift in Computer‐Aided Molecular Design and Property Predictions

    WIREs Computational Molecular Science2025journal article

    View DOI
    0 cites

  10. A new approach methodology (NAM) for carcinogenicity prediction of organic chemicals using the multiclass ARKA framework and machine-learning-based stacking regression

    Journal of Hazardous Materials2025journal article

    View DOI
    0 cites

  11. 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 Environment2025journal article

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    0 cites

  12. A bibliometric analysis of the Cheminformatics/QSAR literature (2000–2023) for predictive modeling in data science using the SCOPUS database

    Molecular Diversity2025journal article

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    0 cites

  13. The q-RASPR approach for predicting the property and fate of persistent organic pollutants

    Scientific Reports2025journal article

    View DOI
    0 cites

  14. The round-robin approach applied to nanoinformatics: consensus prediction of nanomaterials zeta potential

    Beilstein Journal of Nanotechnology2024journal article

    View DOI
    0 cites

  15. Organic Sunscreens and Their Products of Degradation in Biotic and Abiotic Conditions—In Silico Studies of Drug-Likeness and Human Placental Transport

    International Journal of Molecular Sciences2024journal article

    View DOI
    0 cites

  16. 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

    Critical Reviews in Toxicology2024journal article

    View DOI
    0 cites

  17. The application of chemical similarity measures in an unconventional modeling framework c-RASAR along with dimensionality reduction techniques to a representative hepatotoxicity dataset

    Scientific Reports2024journal article

    View DOI
    0 cites

  18. Breaking the Barriers: Machine-Learning-Based c-RASAR Approach for Accurate Blood–Brain Barrier Permeability Prediction

    Journal of Chemical Information and Modeling2024journal article

    View DOI
    0 cites

  19. The Round Robin approach applied to nanoinformatics: consensus prediction of nanomaterials zeta potential

    2024preprint

    View DOI
    0 cites

  20. ARKA: A framework of dimensionality reduction for machine-learning classification modeling, risk assessment, and data gap-filling of sparse environmental toxicity data

    2024preprint

    View DOI
    0 cites

  21. q-RASAR Modeling of Cytotoxicity of TiO<sub>2</sub>-based Multi-component Nanomaterials

    2022working paper

    View DOI
    0 cites

  22. Cliffs in Biological Activity Landscape

    1970book chapter

    View DOI
    0 cites

  23. Comparative machine learning and deep learning frameworks for robust carcinogenicity prediction and activity cliff analysis

    Environmental Science: Processes & Impacts1970journal article

    View DOI
    0 cites

  24. Activity Cliffs

    1970book

    View DOI
    0 cites

  25. Activity Cliffs and Dataset Modelability: Future Roadmaps

    1970book chapter

    View DOI
    0 cites

  26. Applications of Quantitative Read-Across Structure–Property Relationship (q-RASPR) Modeling in the Field of Materials Science

    1970book chapter

    View DOI
    0 cites

  27. Outliers and the Applicability Domain of QSAR Models

    1970book chapter

    View DOI
    0 cites

  28. QSAR: A Tool of Predictive Cheminformatics

    1970book chapter

    View DOI
    0 cites

  29. The Arithmetic Residuals in K-Groups Analysis (ARKA) for the Detection of Activity Cliffs

    1970book chapter

    View DOI
    0 cites

  30. The multiclass ARKA framework for developing improved q-RASAR models for environmental toxicity endpoints

    Environmental Science: Processes & Impacts1970journal article

    View DOI
    0 cites

  31. q-RASAR for Predictive Modeling

    1970book chapter

    View DOI
    0 cites

  32. ARKA: a framework of dimensionality reduction for machine-learning classification modeling, risk assessment, and data gap-filling of sparse environmental toxicity data

    Environmental Science: Processes & Impacts1970journal article

    View DOI
    0 cites

  33. Innovative strategies for the quantitative modeling of blood–brain barrier (BBB) permeability: harnessing the power of machine learning-based q-RASAR approach

    Molecular Systems Design & Engineering1970journal article

    View DOI
    0 cites

  34. QSPR and q-RASPR predictions of the adsorption capacity of polyethylene, polypropylene and polystyrene microplastics for various organic pollutants in diverse aqueous environments

    Environmental Science: Nano1970journal article

    View DOI
    0 cites

  35. Machine learning - based q-RASAR modeling to predict acute contact toxicity of binary organic pesticide mixtures in honey bees

    Journal of Hazardous Materials1970journal article

    View DOI
    0 cites

  36. 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

    Chemical Research in Toxicology1970journal article

    View DOI
    0 cites

  37. 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 Systems1970journal article

    View DOI
    0 cites

  38. A machine learning q-RASPR approach for efficient predictions of the specific surface area of perovskites**

    Molecular Informatics1970journal article

    View DOI
    0 cites

  39. Efficient predictions of cytotoxicity of TiO<sub>2</sub>-based multi-component nanoparticles using a machine learning-based q-RASAR approach

    Nanotoxicology1970journal article

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    0 cites

  40. Machine learning-based q-RASPR modeling of power conversion efficiency of organic dyes in dye-sensitized solar cells

    Sustainable Energy and Fuels1970journal article

    View DOI
    0 cites

  41. Machine learning-based q-RASPR predictions of detonation heat for nitrogen-containing compounds

    Materials Advances1970journal article

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    0 cites

  42. Read-Across and RASAR Tools from the DTC Laboratory

    Challenges and Advances in Computational Chemistry and Physics1970book chapter

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    0 cites

  43. 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

    Environmental Science: Processes and Impacts1970journal article

    View DOI
    0 cites

  44. Quick and efficient quantitative predictions of androgen receptor binding affinity for screening Endocrine Disruptor Chemicals using 2D-QSAR and Chemical Read-Across

    Chemosphere1970journal article

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    0 cites

  45. First report of q-RASAR modeling toward an approach of easy interpretability and efficient transferability

    Molecular Diversity1970journal article

    View DOI
    0 cites

  46. Quantitative predictions from chemical read-across and their confidence measures

    Chemometrics and Intelligent Laboratory Systems1970journal article

    View DOI
    0 cites

  47. Quantitative Predictions from Chemical Read-Across and Their Confidence Measures

    ChemRxiv1970other

    View DOI
    0 cites

  48. Quick and Efficient Quantitative Predictions of Androgen Receptor Binding Affinity for Screening Endocrine Disruptor Chemicals Using 2D-QSAR and Chemical Read-Across

    ChemRxiv1970other

    View DOI
    0 cites

  49. First report of q-RASAR modeling towards an approach of easy interpretability and efficient transferability

    ChemRxiv1970other

    View DOI
    0 cites

  50. A machine learning q-RASPR approach for efficient predictions of the specific surface area of perovskites

    ChemRxiv1970other

    View DOI
    0 cites

  51. A novel quantitative read-across tool designed purposefully to fill the existing gaps in nanosafety data

    Environmental Science: Nano1970journal article

    View DOI
    0 cites