Mining Toxicity Information from Large Amounts of Toxicity Data

Wu Zhenxing; Jiang Dejun; Wang Jike; Hsieh Chang-Yu; Cao Dongsheng; Hou Tingjun

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Mining Toxicity Information from Large Amounts of Toxicity Data

机译：来自大量毒性数据的挖掘毒性信息

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Safety is a main reason for drug failures, and therefore, the detection of compound toxicity and potential adverse effects in the early stage of drug development is highly desirable. However, accurate prediction of many toxicity endpoints is extremely challenging due to low accessibility of sufficient and reliable toxicity data, as well as complicated and diversified mechanisms related to toxicity. In this study, we proposed the novel multitask graph attention (MGA) framework to learn the regression and classification tasks simultaneously. MGA has shown excellent predictive power on 33 toxicity data sets and has the capability to extract general toxicity features and generate customized toxicity fingerprints. In addition, MGA provides a new way to detect structural alerts and discover the relationship between different toxicity tasks, which will be quite helpful to mine toxicity information from large amounts of toxicity data.

机译：安全性是药物失败的主要原因，因此，在药物开发的早期阶段检测化合物毒性和潜在不良反应是非常可取的。然而，由于缺乏足够可靠的毒性数据，以及与毒性相关的复杂多样的机制，因此准确预测许多毒性终点极具挑战性。在本研究中，我们提出了新的多任务图注意（MGA）框架来同时学习回归和分类任务。MGA已在33个毒性数据集上显示出良好的预测能力，并具有提取一般毒性特征和生成定制毒性指纹的能力。此外，MGA提供了一种新的方法来检测结构警报，并发现不同毒性任务之间的关系，这将有助于从大量毒性数据中挖掘毒性信息。

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来源
《Journal of Medicinal Chemistry》 |2021年第10期|共13页
作者
Wu Zhenxing; Jiang Dejun; Wang Jike; Hsieh Chang-Yu; Cao Dongsheng; Hou Tingjun;
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作者单位

Zhejiang Univ Coll Pharmaceut Sci Innovat Inst Artificial Intelligence Med Hangzhou 310058 Zhejiang Peoples R China;

Zhejiang Univ Coll Pharmaceut Sci Innovat Inst Artificial Intelligence Med Hangzhou 310058 Zhejiang Peoples R China;

Zhejiang Univ Coll Pharmaceut Sci Innovat Inst Artificial Intelligence Med Hangzhou 310058 Zhejiang Peoples R China;

Tencent Quantum Lab Shenzhen 518057 Guangdong Peoples R China;

Cent South Univ Xiangya Sch Pharmaceut Sci Changsha 410004 Hunan Peoples R China;

Zhejiang Univ Coll Pharmaceut Sci Innovat Inst Artificial Intelligence Med Hangzhou 310058 Zhejiang Peoples R China;

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正文语种 eng
中图分类药学;
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Mining Toxicity Information from Large Amounts of Toxicity Data

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