预测血溶性和它们的血溶性度
Anand Singh Rathore1, Nishant Kumar1, Shubham Choudhury1
1Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
Communications biology
|February 5, 2025
概括
这项研究引入了模型来预测和量化的溶血活性,这是药物失败的常见原因. 开发的HemoPI2工具通过预测血液溶解度 (HC50) 来帮助设计更安全的基于的治疗方法.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 基于的药物经常因毒性而遭遇临床试验失败,特别是针对红细胞 (RBC) 的血液溶解活性.
- 目前用于血液溶解的预测方法无法量化诱导50%的红细胞溶解所需的特定度 (HC50).
- 了解和预测血液溶解活性对于开发更安全的疗法至关重要.
研究的目的:
- 开发和验证用于识别血溶性和量化其血溶性度 (HC50) 的计算模型.
- 为研究人员创建一个用户友好的平台,以设计具有可预测的溶血活性的.
- 推进类药物安全性概况的预测.
主要方法:
- 在1926年的数据集上训练的分类和回归模型的开发,具有实验确定的HC50值.
- 利用机器学习 (ML),量子ML和蛋白质语言模型,包括结合随机森林 (RF) 和基于动机的分析的混合方法.
- 氨基酸性质的分析,确定疏水性/正电荷残留物与增加的溶血活性之间的关联.
主要成果:
- 一种混合分类模型实现了高AUROC的0.921,超过了其他ML和蛋白质语言模型.
- 回归模型表现出强的表现,皮尔森相关系数 (R) 为0.739和R2为0.543.
- 开发的模型显著优于预测分离活性的现有方法.
结论:
- 计算模型可以有效地预测和量化的溶血活性 (HC50).
- HemoPI2平台提供了一个有价值的工具,可以通过预测HC50值来设计更安全的基于的治疗方法.
- 这项工作有助于缓解因毒性引起的药物开发失败.
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