The blood–brain barrier plays a crucial role in regulating the passage of 98% of the compounds that enter the central nervous system (CNS). Compounds with high permeability must be identified to enable the synthesis of brain medications for the treatment of various brain diseases, such as Parkinson’s, Alzheimer’s, and brain tumors. Throughout the years, several models have been developed to solve this problem and have achieved acceptable accuracy scores in predicting compounds that penetrate the blood–brain barrier. However, predicting compounds with “low” permeability has been a challenging task. In this study, we present a deep learning (DL) classification model to predict blood–brain barrier permeability. The proposed model addresses the fundamental issues presented in former models: high dimensionality, class imbalances, and low specificity scores. We address these issues to enhance the high-dimensional, imbalanced dataset before developing the classification model: the imbalanced dataset is addressed using oversampling techniques and the high dimensionality using a non-linear dimensionality reduction technique known as kernel principal component analysis (KPCA). This technique transforms the high-dimensional dataset into a low-dimensional Euclidean space while retaining invaluable information. For the classification task, we developed an enhanced feed-forward deep learning model and a convolutional neural network model. In terms of specificity scores (i.e., predicting compounds with low permeability), the results obtained by the enhanced feed-forward deep learning model outperformed those obtained by other models in the literature that were developed using the same technique. In addition, the proposed convolutional neural network model surpassed models used in other studies in multiple accuracy measures, including overall accuracy and specificity. The proposed approach solves the problem inevitably faced with obtaining low specificity resulting in high false positive rate.

中文翻译：

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一种预测血脑屏障通透性的深度学习方法

血脑屏障在调节进入中枢神经系统 (CNS) 的 98% 的化合物通过方面起着至关重要的作用。必须确定具有高渗透性的化合物才能合成用于治疗各种脑部疾病的脑药物，例如帕金森氏症、阿尔茨海默氏症和脑肿瘤。多年来，已经开发了几种模型来解决这个问题，并在预测穿透血脑屏障的化合物方面取得了可接受的准确度分数。然而，预测具有“低”渗透性的化合物一直是一项具有挑战性的任务。在这项研究中，我们提出了一个深度学习 (DL) 分类模型来预测血脑屏障通透性。提出的模型解决了以前模型中存在的基本问题：高维、类别不平衡、和低特异性分数。在开发分类模型之前，我们解决了这些问题以增强高维不平衡数据集：不平衡数据集使用过采样技术解决，高维使用称为核主成分分析 (KPCA) 的非线性降维技术解决。该技术将高维数据集转换为低维欧几里得空间，同时保留了宝贵的信息。对于分类任务，我们开发了增强型前馈深度学习模型和卷积神经网络模型。在特异性评分方面（即预测具有低渗透性的化合物），增强型前馈深度学习模型获得的结果优于文献中使用相同技术开发的其他模型获得的结果。此外，所提出的卷积神经网络模型在多个准确度度量（包括整体准确度和特异性）方面超过了其他研究中使用的模型。所提出的方法解决了不可避免地面临获得低特异性导致高假阳性率的问题。