As the number of man-made chemicals increases at an unprecedented pace, efforts of quickly screening and accurately evaluating their potential adverse biological effects have been hampered by prohibitively high costs of in vivo/vitro toxicity testing. While it is unrealistic and unnecessary to test every uncharacterized chemical, it remains a major challenge to develop alternative in silico tools with high reliability and precision in toxicity prediction. To address this urgent need, we have developed a novel mode-of-action-guided, molecular modeling-based, and machine learning-enabled modeling approach for in silico chemical toxicity prediction. Here we introduce the core element of this approach, Target-specific Toxicity Knowledgebase (TsTKb), which consists of two main components: Chemical Mode of Action (ChemMoA) database and a suite of prediction model libraries.

中文翻译：

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目标特异性毒性知识库（TsTKb）：用于计算机预测毒理学的新型工具包。

由于人造化学药品的数量以前所未有的速度增长，体内/体外毒性测试的高昂成本阻碍了快速筛选和准确评估其潜在的不利生物学效应的努力。尽管测试每种未表征的化学物质是不现实且不必要的，但开发具有高可靠性和精确度的毒性预测方法的替代计算机硅工具仍然是一项重大挑战。为了满足这一迫切需求，我们开发了一种新颖的作用模式指导，基于分子模型的，基于机器学习的建模方法，用于计算机化学毒性预测。在这里，我们介绍这种方法的核心要素，即针对特定目标的毒性知识库（TsTKb），它由两个主要组件组成：