基于功能,环境和健康风险构建抗抑郁药物优先清单,使用可解释的混合转换器深度学习模型
Peixuan Sun1, Huaishi Liu2, Yuanyuan Zhao1
1College of New Energy and Environment, Jilin University, Changchun 130012, China.
Journal of hazardous materials
|June 6, 2024
概括
新出现的抗抑郁药 (AD) 污染物需要紧急进行风险评估. 这项研究开发了ADRank,这是一个使用深度学习来识别高风险AD的系统,揭示了影响毒性和受体相互作用的结构因素,以便更好地管理.
科学领域:
- 环境化学环境化学
- 毒理学 毒理学 毒理学
- 计算化学的计算化学
背景情况:
- 抗抑郁药 (ADs) 是新出现的污染物,需要进行风险评估.
- 目前用于优先考虑AD的方法不足以进行全面的风险评估.
研究的目的:
- 为抗抑郁药物开发一个全面的风险优先查系统 (ADRank).
- 确定高优先级的ADS并分析结构-毒性关系.
主要方法:
- 使用集成分配方法和深度学习分类模型 (改进的混合变压器) 构建了ADRank.
- 用随机森林和TOPSIS方法比较模型准确性.
- 使用了SHapley添加剂解释 (SHAP) 可视化,分子动力学和氨基酸分析.
主要成果:
- 深度学习模型的准确性高达23.25%,高于随机森林.
- 该ADRank系统确定了33种优先级抗抑郁药.
- 较高的分子内键,疏水性和电子阴性与AD风险增加相关.
- 范德瓦尔斯和静电相互作用是AD受体结合的关键因素.
结论:
- ADRank提供了一个有效的系统来优先考虑抗抑郁剂污染物.
- 了解结构毒性关系有助于风险管理和环境安全.
- 该研究为开发更安全的药物化合物和管理环境风险提供了见解.
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