基于贝叶斯模型预测类药物的肝毒性

Han-Ha Chai, Wonchoul Park, Yeoung-Bae Jin, Dajeong Lim
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引用次数: 0

摘要

预测肝毒性是类药物安全性评估的重要组成部分。肝毒性与类药物的理化性质有关,尤其是其结构警报。在本研究中,我们根据类药物的定量结构-毒性关系(QSAR)建立了一个贝叶斯模型来预测 498 种类药物的肝毒性。所设计的模型利用 25 个结构描述因子(如 ECFP6 指纹)预测了这些化合物的肝毒性,并提供了灵敏度、特异性和灵敏度
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting Hepatotoxicity of Drug-like compounds based on Bayesian model
Predicting hepatotoxicity is an important component of safety-related evaluations of drug-like compounds. Hepatotoxicity is related to the physicochemical properties of drug-like compounds, especially their structural alerts. In this study, we developed a Bayesian model to predict the hepatotoxicities of 498 drug-like compounds based on their quantitative structure-toxicity relationships (QSAR). The devised model predicted the hepatotoxicity of these compounds using 25 structural descriptors (such as the ECFP6 fingerprint) and provided a sensitivity, specificity
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