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Updated: Jan 13, 2026

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
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通过基于SHAP的解释来预测ERα对手的多目标QSAR预测

Jinhui Cao1, Yanli Liu1,2

  • 1School of Science, Wuhan University of Science and Technology, Wuhan, China.

PloS one
|January 8, 2026
PubMed
概括

本研究介绍了一种用于药物发现的两阶段机器学习框架,使用定量结构-活性关系 (QSAR) 建模和一种新的双选特征 (DFFS) 方法. 该方法有效预测药物活性和ADMET特性,增强候选药物的评估.

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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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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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