Predictive modelling of peroxisome proliferator-activated receptor gamma (PPARγ) IC50 inhibition by emerging pollutants using light gradient boosting machine.

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Tác giả: O Adesina, A Awomuti, A W Mumbi, O W Samuel, D Yin, Z Yu

Ngôn ngữ: eng

Ký hiệu phân loại: 922.945 *Hindus

Thông tin xuất bản: England : SAR and QSAR in environmental research , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 742620

Peroxisome proliferator-activated receptor gamma (PPARγ), a critical nuclear receptor, plays a pivotal role in regulating metabolic and inflammatory processes. However, various environmental contaminants can disrupt PPARγ function, leading to adverse health effects. This study introduces a novel approach to predict the inhibitory activity (IC50 values) of 140 chemical compounds across 13 categories, including pesticides, organochlorines, dioxins, detergents, flame retardants, and preservatives, on PPARγ. The predictive model, based on the light-gradient boosting machine (LightGBM) algorithm, was trained on a dataset of 1804 molecules showed
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