早期药物发现中的hERG毒性预测使用极端梯度增强和同位分层组合映射
Gabriela Falcón-Cano1, Aliuska Morales-Helguera1, Heather Lambert1
1PIKAÏROS, S.A, 31650, Saint Orens de Gameville, France.
Scientific reports
|May 4, 2025
概括
这项研究引入了一种增强的机器学习模型 (XGBoost + ISE地图),用于预测人类以太基因相关基因 (hERG) 通道抑制,这对于预防药物发现期间药物诱导的心脏毒性至关重要.
科学领域:
- 药理学和毒理学 药理学和毒理学
- 计算化学计算化学
- 机器学习在药物发现中的应用
背景情况:
- 人类以太基因相关基因 (hERG) 通道被小分子阻塞可能导致致命的心脏毒性.
- 由于心脏副作用而导致的药物戒断突显了早期hERG毒性识别的必要性.
- 现有的机器学习模型在稳定性,类不平衡性和可解释性方面面临挑战.
研究的目的:
- 开发一个可靠和可解释的机器学习模型来预测hERG通道抑制.
- 在早期药物发现中改进潜在心脏毒性化合物的识别.
- 利用最大的公共hERG抑制数据库进行增强的预测.
主要方法:
- 整合极端梯度增强 (XGBoost) 与同位分层集群 (ISE) 地图 (XGB + ISE 地图) 的整合.
- 开发一个XGBoost共识模型,使用平衡的训练集和多样化的变量子集.
- 应用ISE映射用于适用性领域估计和预测信心评估.
主要成果:
- XGBoost + ISE地图模型表现出强大的性能,具有高灵敏度 (0.83) 和特异性 (0.90).
- 通过数据分层,ISE映射提高了预测信心和化合物选择.
- 变量重要性分析确定了与hERG抑制相关的关键分子决定因素 (例如,peoe_VSA8,ESOL).
结论:
- XGBoost + ISE 地图策略为预测hERG抑制提供了一种有效的方法.
- 该方法有助于识别具有降低心脏毒性风险的有希望的候选药物.
- 增强的可解释性和稳定性解决了当前hERG预测模型中的关键挑战.
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