Abstract：
It is well-known that the hepatotoxicity of drugs can significantly influence their clinical use. Despite their effective therapeutic efficacy, many drugs are severely limited in clinical applications due to significant hepatotoxicity. In response, researchers have created several machine learning-based hepatotoxicity prediction models for use in drug discovery and development. Researchers aim to predict the potential hepatotoxicity of drugs to enhance their utility. However, current hepatotoxicity prediction models often suffer from being unverified, and they fail to capture the detailed toxicological structures of predicted hepatotoxic compounds. Using the 56 chemical constituents of Gardenia jasminoides as examples, we validated the trained hepatotoxicity prediction model through literature reviews, principal component analysis (PCA), and structural comparison methods. Ultimately, we successfully developed a model with strong predictive performance and conducted visual validation. Interestingly, we discovered that the predicted hepatotoxic chemical constituents of Gardenia possess both toxic and therapeutic effects, which are likely dose-dependent. This discovery greatly contributes to our understanding of the dual nature of drug-induced hepatotoxicity.
摘要：
众所周知，药物的肝毒性可以显着影响其临床使用。尽管它们有效的治疗效果，由于严重的肝毒性，许多药物在临床应用中受到严重限制。作为回应,研究人员已经创建了几个基于机器学习的肝毒性预测模型，用于药物发现和开发。研究人员旨在预测药物的潜在肝毒性以增强其效用。然而,目前的肝毒性预测模型往往无法得到验证，它们无法捕获预测的肝毒性化合物的详细毒理学结构。以栀子的56种化学成分为例,我们通过文献综述验证了训练后的肝毒性预测模型，主成分分析(PCA)，和结构比较方法。最终,我们成功开发了一个具有强大预测性能的模型，并进行了视觉验证。有趣的是,我们发现，栀子的预测肝毒性化学成分具有毒性和治疗作用，可能是剂量依赖性的。这一发现极大地有助于我们对药物诱导的肝毒性的双重性质的理解。
