Výsledky vyhledávání - "Arkaprava Banerjee"

Akademický článek

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

Autor: Arkaprava Banerjee, Kunal Roy

Publikováno v: Scientific Reports, Vol 14, Iss 1, Pp 1-22 (2024)

Abstract With the exponential progress in the field of cheminformatics, the conventional modeling approaches have so far been to employ supervised and unsupervised machine learning (ML) and deep learning models, utilizing the standard molecular descr

Externí odkaz: https://doaj.org/article/e65a533ecb4744a0b4d7b14c87612d0e

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

Autor: Arkaprava Banerjee, Kunal Roy

The advancements in the field of cheminformatics have led to a reduction in animal testing to estimate the activity/property/toxicity of query chemicals. Read-Across Structure-Activity Relationship (RASAR) is an emerging concept that utilizes various

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b52839672251a1b7f4175ec694a049d8
https://doi.org/10.26434/chemrxiv-2023-20v0k

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

Autor: Kunal Roy, Priyanka De, Mainak Chatterjee, Arkaprava Banerjee, Agnieszka Gajewicz-Skretna

Publikováno v: Environmental Science: Nano. 9:189-203

In the current study, we propose a new quantitative read-across methodology for predicting the toxicity of newly synthesized NPs based on the similarity with structural analogues.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4fef9ab16b34eeb4b571f44c998ef13b
https://doi.org/10.1039/d1en00725d

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

Autor: Arkaprava Banerjee, Agnieszka Gajewicz‐Skretna, Kunal Roy

In this study, the specific surface area of various perovskites was modeled using a novel quantitative read-across structure-property relationship (q-RASPR) approach, which clubs both Read-Across (RA) and quantitative structure-property relationship

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5222872be748606dbe6a5bddf940e6c6
https://doi.org/10.26434/chemrxiv-2022-cvjg7

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q-RASAR Modeling of Cytotoxicity of TiO2-based Multi-component Nanomaterials

Autor: Arkaprava Banerjee, Supratik Kar, Agnieszka Gajewicz-Skretna, Kunal Roy

Read-Across Structure-Activity Relationship (RASAR) is an emerging cheminformatic approach that combines the usefulness of a QSAR model and similarity-based Read-Across predictions. In this work, we have generated a simple, interpretable, and transfe

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::fc77d261f6e76ce59a3fc2e0dec3ace2
https://doi.org/10.20944/preprints202210.0402.v1

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Quantitative Predictions from Chemical Read-Across and Their Confidence Measures

Autor: Arkaprava Banerjee, Mainak Chatterjee, Priyanka De, Kunal Roy

In silico modeling new approach methodologies (NAMs) are viewed as a promising starting point for filling the existing gaps in safety and ecosafety data. Read-across is one of the most widely used alternative tools for hazard assessment, aimed at fil

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f658d86d550876c89ebdbf01dcadec2a
https://doi.org/10.26434/chemrxiv-2022-4s53w

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

Autor: Arkaprava Banerjee, Priyanka De, Vinay Kumar, Supratik Kar, Kunal Roy

Publikováno v: Chemosphere. 309(Pt 1)

Endocrine Disruptor Chemicals are synthetic or natural molecules in the environment that promote adverse modifications of endogenous hormone regulation in humans and/or in animals. In the present research, we have applied two-dimensional quantitative

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5362396ee8e7511619ec87cfd6a50a9b
https://pubmed.ncbi.nlm.nih.gov/36174732

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