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The Blood-brain Barrier00:49

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Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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一个全面的机器学习框架,用于预测血脑屏障的透性,通过殖民地优化进行增强.

Ahmed Miloudi1, Mohamed Chikri1, Said Boujraf1

  • 1Sciences médicales et pharmaceutiques et recherche translationnelle, Faculty of Medicine and Pharmacy, Sidi Mohamed Ben Abdellah University, Fez, Morocco.

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概括
此摘要是机器生成的。

这项研究开发了一种殖民地优化优化的深度神经网络 (ACO-DNN),用于预测血脑屏障 (BBB) 透性. ACO-DNN取得了最先进的结果,增强了中枢神经系统药物发现虚拟查.

关键词:
血液-大脑屏障是什么?殖民地优化殖民地优化深度神经网络是一个神经网络.药物发现. 药物发现.机器学习是机器学习.

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科学领域:

  • 计算化学是一种计算化学.
  • 机器学习在药物发现中的作用
  • 神经科学是一个神经科学.

背景情况:

  • 血脑屏障 (BBB) 限制了药物输送到中枢神经系统 (CNS).
  • 准确的BBB透性的in silico预测对于有效的药物发现至关重要.
  • 开发强大的预测模型对于克服BBB挑战至关重要.

研究的目的:

  • 开发和优化用于预测BBB透性的机器学习框架.
  • 使用殖民地优化 (ACO) 增强深度神经网络 (DNN) 模型.
  • 为中枢神经系统药物发现中虚拟查提供准确的工具.

主要方法:

  • 策划了1957个化合物的数据集,其中有212个分子描述符.
  • 采用基于支架的分割来进行数据集分区.
  • 基准基准模型包括随机森林和标准DNN.
  • 使用ACO来系统地优化DNN的超参数.

主要成果:

  • 随机森林实现了基线AUC的0.913.
  • ACO算法确定了一个优化的DNN架构.
  • ACO优化的DNN实现了0.921.92的最先进的AUC-ROC.
  • 优化模型的表现优于所有基线分类器.

结论:

  • 超听觉优化 (ACO) 与传统的超参数调整相比,提供了优势.
  • ACO优化的DNN表现出稳定性和强大的泛化能力.
  • 开发的方法是药物发现中复杂模型优化的可通用模板.