交叉融合-XGBoost:通过多功能融合,交叉验证选和极端梯度增强,准确预测最高推的每日剂量
Qiang Li1, Yu He1, Jianbo Pan1
1Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China.
Briefings in bioinformatics
|January 12, 2024
概括
一个新的CrossFuse极端梯度增强 (XGBoost) 模型使用人类数据预测新药的最大建议每日剂量. 这种方法通过提高剂量选择和预测准确度,提高了首次在人体 (FIH) 试验的安全性.
科学领域:
- 药物发现和开发 药物发现和开发
- 计算化学计算化学
- 药理学 药理学是指药理学的学科.
背景情况:
- 药物安全问题在很大程度上导致开发失败,首次在人体 (FIH) 试验具有很高的风险.
- 在临床试验中选择初始药物剂量的传统方法依赖于动物对人类的推断,由于物种间的差异,这种推断可能不可靠.
- 早期临床试验中的灾难性事件凸显了需要改进的方法来预测人类药物反应和安全剂量.
研究的目的:
- 开发一种新的计算方法来预测化合物的最大建议每日剂量 (MRDD).
- 为在首次在人体 (FIH) 试验中选择安全的初始剂量提供可靠的参考.
- 与现有方法相比,提高剂量预测的准确性和覆盖率.
主要方法:
- 提出了一个CrossFuse极端梯度增强 (XGBoost) 模型,集成分子表示,物理化学性质和化合物-蛋白质相互作用.
- 采用交叉验证来改进特征选择,确定影响MRDD的关键因素.
- 利用现有的人类研究数据直接预测化合物MRDD.
主要成果:
- 与k-近邻 (k-NN) 和可变k-NN (v-NN) 方法相比,CrossFuse-XGBoost方法显示出更高的预测准确度.
- 在外部验证集上实现了完整的预测覆盖,解决了以前模型的局限性.
- 确定了影响MRDD的241个关键特征,为复合结构-活动关系提供了洞察力.
结论:
- 交叉融合-XGBoost模型提供了一种更可靠和更准确的方法,用于预测早期开发中的安全药物剂量.
- 该方法的可解释性为优化新化合物结构和指导实验研究提供了宝贵的指导.
- 自由可用的数据集和源代码促进了更广泛的采用和进一步研究计算药物安全.
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