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Updated: May 7, 2025

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
00:05

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

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使用机器学习的QSAR分类建模,用于多变量化学危险终点的基于共识的方法

Yunendah Nur Fuadah1,2, Muhammad Adnan Pramudito1, Lulu Firdaus1

  • 1Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea.

ACS omega
|January 1, 2025
PubMed
概括

这项研究提出了一种混合机器学习方法,用于在八个终点上预测化学毒性. 该计算模型表现出强大的预测性能,为化学安全评估提供了可靠的in silico方法.

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Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

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Author Spotlight: High-Throughput Toxicity Screening Using Zebrafish Embryo Startle Response Assay

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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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