药物诱导的肝毒性的一种多维计算框架:将分子结构特征与疾病发病因子结合起来
Huayu Zhong1, Juanji Wang1, Xiaoxiao Liu1
1College of Pharmacy, Chongqing Medical University, No. 1 Yixueyuan Road, Yuzhong District, Chongqing 400016, P. R. China.
Briefings in bioinformatics
|September 8, 2025
概括
这项研究引入了一个计算框架来预测药物诱导的肝损伤,特别是药物诱导的肝内胆固醇. 该方法整合了分子结构和疾病途径,在识别有毒化合物方面实现了高精度.
科学领域:
- 计算毒理学和化学信息学
- 药理学和药物发现
- 系统生物学和生物信息学
背景情况:
- 药物诱导性肝毒性 (DIH) 呈现出多样化的表型和复杂的机制,在药物开发中构成重大挑战.
- 了解药物特性与肝损伤之间的复杂关系对于确保药物安全至关重要.
- 药物诱导的肝内胆固醇症 (DIIC) 是一种特定的,但至关重要的DIH亚型,需要集中调查.
研究的目的:
- 开发和验证一个多维计算框架,用于系统地预测药物诱导的肝损伤,重点是DIIC.
- 将分子结构分析与疾病致病性探索相结合,以解码DIH复杂性.
- 确定与DIIC病原发生相关的关键分子特征和途径,以改善药物安全性评估.
主要方法:
- 采用基于图形的模块化最大化算法来识别DIIC风险基因和疾病发病集群.
- 计算了药物目标和DIIC集群之间的网络近距离值,以确定药物疾病关系.
- 开发了使用随机森林和K-最近邻近图形卷积网络的预测模型,集成分子描述符,结构警报 (SAs) 和网络近距离.
主要成果:
- 随机森林模型实现了高的DIIC预测准确性 (ACC=0.740±0.014,AUC=0.828±0.008). 随机森林模型可以实现高的DIIC预测准确性.
- 一个K-近邻图卷积网络模型在药物分类方面表现出卓越的性能 (ACC=0.810±0.024,AUC=0.890±0.014).
- 机械分析将特定的结构性警报 (,-硫异原子链,基团) 与DIIC病变发生联系起来,包括对新陈代谢和脂质调节的影响.
结论:
- 拟议的多维计算框架有效地将分子特征和疾病机制连接起来,用于毒性预测.
- 这种方法为以途径为中心的药物安全性评估提供了一个可通用的策略,特别是在像DIIC这样的复杂疾病中.
- 这些发现为DIIC的分子基础提供了宝贵的见解,有助于开发更安全的治疗方法.
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