Arkaprava Banerjee

1.1k total citations · 1 hit paper
32 papers, 659 citations indexed

About

Arkaprava Banerjee is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Analytical Chemistry. According to data from OpenAlex, Arkaprava Banerjee has authored 32 papers receiving a total of 659 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Computational Theory and Mathematics, 9 papers in Materials Chemistry and 6 papers in Analytical Chemistry. Recurrent topics in Arkaprava Banerjee's work include Computational Drug Discovery Methods (22 papers), Machine Learning in Materials Science (8 papers) and Spectroscopy and Chemometric Analyses (6 papers). Arkaprava Banerjee is often cited by papers focused on Computational Drug Discovery Methods (22 papers), Machine Learning in Materials Science (8 papers) and Spectroscopy and Chemometric Analyses (6 papers). Arkaprava Banerjee collaborates with scholars based in India, Italy and United States. Arkaprava Banerjee's co-authors include Kunal Roy, Mainak Chatterjee, Priyanka De, Agnieszka Gajewicz, Vinay Kumar, Supratik Kar, Emilio Benfenati, Edoardo Carnesecchi, Simone Tosi and Paola Gramatica and has published in prestigious journals such as The Science of The Total Environment, Journal of Hazardous Materials and Scientific Reports.

In The Last Decade

Arkaprava Banerjee

30 papers receiving 654 citations

Hit Papers

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What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

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

2024 40 citations Arkaprava Banerjee, Kunal Roy Environmental Science Processes & Impacts profile →

Peers

Countries citing papers authored by Arkaprava Banerjee

Since Specialization

Citations

This map shows the geographic impact of Arkaprava Banerjee's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Arkaprava Banerjee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Arkaprava Banerjee more than expected).

Fields of papers citing papers by Arkaprava Banerjee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Arkaprava Banerjee. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Arkaprava Banerjee. The network helps show where Arkaprava Banerjee may publish in the future.

Co-authorship network of co-authors of Arkaprava Banerjee

This figure shows the co-authorship network connecting the top 25 collaborators of Arkaprava Banerjee. A scholar is included among the top collaborators of Arkaprava Banerjee based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Arkaprava Banerjee. Arkaprava Banerjee is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	From Feature‐Based Chemical Similarity to Chemical Language Models—A Paradigm Shift in Computer‐Aided Molecular Design and Property Predictions 2025 ·Wiley Interdisciplinary Reviews Computational Molecular Science ·Arkaprava Banerjee, Supratik Kar, Kunal Roy, Grace Patlewicz, Imran Shah, P.G. Karamertzanis, Giuseppina Gini, Emilio Benfenati	0
2	Machine learning assisted classification RASAR modeling for the nephrotoxicity potential of a curated set of orally active drugs 2025 ·Scientific Reports ·Arkaprava Banerjee, Kunal Roy	2
3	Prediction of bioconcentration factors (BCFs) and bioaccumulation factors (BAFs) for per- and polyfluoroalkyl substances (PFASs) using Read-Across and q-RASPR 2025 ·The Science of The Total Environment ·(unknown), Arkaprava Banerjee, Kunal Roy	1
4	The q-RASPR approach for predicting the property and fate of persistent organic pollutants 2025 ·Scientific Reports ·Chia‐Ming Chang, Arkaprava Banerjee, Vinay Kumar, Kunal Roy, Emilio Benfenati	2
5	A new approach methodology (NAM) for carcinogenicity prediction of organic chemicals using the multiclass ARKA framework and machine-learning-based stacking regression 2025 ·Journal of Hazardous Materials ·Arkaprava Banerjee, Kunal Roy	0
6	Machine learning-based q-RASAR predictions of the bioconcentration factor of organic molecules estimated following the organisation for economic co-operation and development guideline 305 2024 ·Journal of Hazardous Materials ·(unknown), (unknown), Mainak Chatterjee, Arkaprava Banerjee, Kunal Roy	9
7	QSPR and q-RASPR predictions of the adsorption capacity of polyethylene, polypropylene and polystyrene microplastics for various organic pollutants in diverse aqueous environments 2024 ·Environmental Science Nano ·(unknown), Arkaprava Banerjee, Mainak Chatterjee, Kunal Roy, M Cronin	4
8	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 2024 ·Critical Reviews in Toxicology ·Arkaprava Banerjee, Supratik Kar, Kunal Roy, Grace Patlewicz, (unknown), Emilio Benfenati, M Cronin	18
9	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) 2024 ·Molecular Informatics ·(unknown), Arkaprava Banerjee, Kunal Roy	10
10	Innovative strategies for the quantitative modeling of blood–brain barrier (BBB) permeability: harnessing the power of machine learning-based q-RASAR approach 2024 ·Molecular Systems Design & Engineering ·Vinay Kumar, Arkaprava Banerjee, Kunal Roy	5
11	ARKA: a framework of dimensionality reduction for machine-learning classification modeling, risk assessment, and data gap-filling of sparse environmental toxicity data breakdown → 2024 ·Environmental Science Processes & Impacts ·Arkaprava Banerjee, Kunal Roy	40
12	Machine learning-based q-RASAR approach for the in silico identification of novel multi-target inhibitors against Alzheimer's disease 2023 ·Chemometrics and Intelligent Laboratory Systems ·Vinay Kumar, Arkaprava Banerjee, Kunal Roy	18
13	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 2023 ·Chemometrics and Intelligent Laboratory Systems ·Arkaprava Banerjee, Kunal Roy	28
14	Machine learning - based q-RASAR modeling to predict acute contact toxicity of binary organic pesticide mixtures in honey bees 2023 ·Journal of Hazardous Materials ·Mainak Chatterjee, Arkaprava Banerjee, Simone Tosi, Edoardo Carnesecchi, Emilio Benfenati, Kunal Roy	29
15	A machine learning q‐RASPR approach for efficient predictions of the specific surface area of perovskites** 2023 ·Molecular Informatics ·Arkaprava Banerjee, Agnieszka Gajewicz, Kunal Roy	21
16	Machine learning-based q-RASPR modeling of power conversion efficiency of organic dyes in dye-sensitized solar cells 2023 ·Sustainable Energy & Fuels ·(unknown), Arkaprava Banerjee, Kunal Roy	17
17	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 2023 ·Environmental Science Processes & Impacts ·Arkaprava Banerjee, Kunal Roy	13
18	Machine learning-based q-RASPR predictions of detonation heat for nitrogen-containing compounds 2023 ·Materials Advances ·(unknown), Arkaprava Banerjee, Kunal Roy	8
19	Quick and efficient quantitative predictions of androgen receptor binding affinity for screening Endocrine Disruptor Chemicals using 2D-QSAR and Chemical Read-Across 2022 ·Chemosphere ·Arkaprava Banerjee, Priyanka De, Vinay Kumar, Supratik Kar, Kunal Roy	27
20	A novel quantitative read-across tool designed purposefully to fill the existing gaps in nanosafety data 2021 ·Environmental Science Nano ·Mainak Chatterjee, Arkaprava Banerjee, Priyanka De, Agnieszka Gajewicz, Kunal Roy	90

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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