组合建模方法用于评估-化物共受体抑制的评估
Julia Kandler1, Ayse Sıla Kantarçeken1, Aljoša Smajić1
1Department of Pharmaceutical Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria.
Journal of chemical information and modeling
|January 23, 2026
概括
环境化学物质抑制酸同载体 (NIS) 可能导致发育神经毒性. 这项研究开发了一个强大的in silico框架,将机器学习和对接结合起来,以预测NIS抑制,以改进毒理风险评估.
科学领域:
- 毒理学 毒理学 毒理学
- 计算化学的计算化学
- 神经科学是一个神经科学.
背景情况:
- 酸同载体 (NIS) 对于甲状腺激素的合成至关重要,对大脑发育至关重要.
- 环境化学物质对NIS的抑制可能导致神经发育障碍,如自闭症和智商降低.
- 准确预测发育性神经毒性 (DNT) 对于风险评估至关重要.
研究的目的:
- 开发和验证用于预测NIS抑制剂的in silico框架.
- 通过模拟NIS抑制来识别具有DNT潜力的环境化学品.
- 支持下一代风险评估策略.
主要方法:
- 应用基于对接的NIS抑制剂的虚拟选.
- 使用ECFP4和CDDD训练的机器学习模型 (RF,XGB,SVM).
- 使用9倍交叉验证和内部测试集验证的模型.
主要成果:
- 结合ML和对接预测改善了歧视 (ROC AUC为0.77).
- 最佳值产生了0.32的MCC和0.78.78的平衡精度.
- 使用1412种不同的化合物开发了一个强大的框架.
结论:
- 这项研究提出了一个新的,强大的计算框架,用于预测NIS抑制.
- 这种方法可以更好地识别导致DNT的化学物质.
- 开发的方法代表了毒理风险评估的新方法.
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