血脑屏障透的预测模型基于带有动态路由的蒙面变压器
Chunwei Ma1,2, Russ Wolfinger1
1JMP Statistical Discovery, LLC, Cary, 27513, NC, USA.
Briefings in bioinformatics
|November 20, 2023
概括
DeepB3P3准确地预测了通过新型深度学习框架穿透血脑屏障的 (BBBPs). 这种计算工具增强了BBBP的发现,克服了传统验证方法的局限性.
科学领域:
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 穿透血脑屏障的 (BBBPs) 对于跨越血脑屏障的药物输送至关重要.
- 对BBPP的实验验证是昂贵和耗时的,需要高效的in silico预测方法.
- 有限的数据可用性和不平衡的数据集对BBBP预测中的当前机器学习模型构成挑战.
研究的目的:
- 开发一个新的计算框架,DeepB3P3,用于准确预测BBBPs.
- 用先进的机器学习技术解决BBBP预测中的数据稀缺和不平衡问题.
- 为BBBP预测提供可靠的不确定性估计.
主要方法:
- 一个集成变压器编码器,卷积神经网络和囊网络架构的深度学习模型.
- 使用蒙面来增强数据增强,以提高有限数据集的模型性能.
- 一种新的值调整方法来处理不平衡的数据和一个强大的不确定性估计方法.
主要成果:
- 在基准数据集上,DeepB3P3实现了高达98.31%的最先进的预测准确度.
- 该框架有效地学习序列特征,并弥补小型训练集大小.
- 对于个别BBBP预测的可靠不确定性估计.
结论:
- DeepB3P3代表了BBBPs计算预测的重大进步.
- 该框架为加速新型BBPP的发现和开发提供了一个有前途的工具.
- DeepB3P3有可能简化针对中枢神经系统的药物发现管道.
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