使用机器学习管道从不同来源寻找胺抑制剂的血
Nivedha Balakrishnan1, Rahul Katkar1, Peter V Pham1
1Department of Chemical and Materials Engineering, San José State University, San Jose, CA 95192, USA.
Bioengineering (Basel, Switzerland)
|November 25, 2023
概括
机器学习模型能够识别出强大的胺抑制剂,即血栓激素的抑制剂,血栓激素是心血管疾病中的关键酶. 这一管道加速了新型直接血栓激素抑制剂 (DTI) 的发现,其疗效和安全性有所改善.
科学领域:
- 生物化学 生化学
- 计算生物学 计算生物学
- 药理学 药理学是指药理学的学科.
背景情况:
- 热血素是心血管疾病发病的一个关键酶.
- 直接血栓抑制剂 (DTIs) 提供了改善的治疗方法,但面临着诸如出血风险和药理动力学等挑战.
- 现有的DTI发现管道需要加强,以提高效率.
研究的目的:
- 开发一条机器学习管道,用于识别和排名具有有效血栓抑制潜力的序列.
- 加快新型直接血栓激素抑制剂 (DTI) 的发现.
主要方法:
- 开发了一个两阶段的机器学习模型,使用已知素抑制剂和非抑制剂的精选数据集.
- 第一个阶段确定了抑制序列 (83.6%的MCC).
- 第二阶段预测了广泛的亲和度的结合亲和度 (日志RMSE为1.114).
主要成果:
- 这些模型确定了血栓抑制剂的独特的物理化学和结构特征.
- 超过1000万个被分类,揭示了具有高预测抑制潜力的有希望的短序列 (<15个氨基酸).
- 根据预测的结合能,确定了假定的DTI候选者.
结论:
- 开发的机器学习管道有效地识别和排名潜在的直接血栓激素抑制剂.
- 这种方法加速了基于的新型DTI的发现,解决了当前治疗方法的局限性.
- 预测管道可以通过Web服务器访问,以获得更广泛的研究应用.
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