基于深度学习的GPR151激活器活动预测分类模型
Huangchao Xu1,2, Baohua Zhang1, Qian Liu3
1Computer Network Information Center, Chinese Academy of Sciences, Dongsheng Sourth Street No.2, Haidian District, Beijing, 100190, China.
BMC bioinformatics
|June 9, 2023
概括
我们开发了一个新的深度学习模型来预测G蛋白结合受体151 (GPR151) 激活器活性. 这种方法提高了药物发现的虚拟查效率,在识别潜在的候选药物方面实现了高准确性.
科学领域:
- 生物化学 生物化学
- 计算化学的计算化学
- 药理学 药理学是指药理学的学科.
背景情况:
- G蛋白结合受体151 (GPR151) 与各种生理和病理过程有关.
- GPR151激活剂显示出对代谢障碍的治疗潜力,需要进一步研究.
- 准确的活动预测模型对于有效的药物发现和虚拟查至关重要.
研究的目的:
- 开发一种可靠的,基于学习的方法来预测GPR151激活器活性.
- 提高在药物发现早期阶段虚拟查的效率.
- 探索新的分子特征提取技术,以提高预测性能.
主要方法:
- 引入了一种新的分子特征提取算法,其灵感来源于词袋模型.
- Mol2vec被用于多种分子特征提取.
- 用三种特征选择算法和三种深度学习模型进行活动预测.
- 评估了五种不同的分类器,包括SVM.
主要成果:
- 拟议的基于深度学习的特征提取方法与传统算法相比,表现出优越的性能.
- 最佳模型Mol2vec-CNN显著提高了分类的准确性和稳定性.
- SVM分类器获得了最高的准确性 (0.92) 和F1得分 (0.76).
结论:
- 开发的深度学习模型对于GPR151激活器活动预测是有效的.
- 特性提取算法在活动预测任务中表现优于传统方法.
- 该模型在药物虚拟查的预选阶段显示出有希望的实用性.
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