PDF(Pubmed)
Abstract：
Diseases related to the central nervous system (CNS) are major health concerns and have serious social and economic impacts. Developing new drugs for CNS-related disorders presents a major challenge as it actively involves delivering drugs into the CNS. Therefore, it is imperative to develop in silico methodologies to reliably identify potential lead compounds that can penetrate the blood-brain barrier (BBB) and help to thoroughly understand the role of different physicochemical properties fundamental to the BBB permeation of molecules. In this study, we have analysed the chemical space of the CNS drugs and compared it to the non-CNS-approved drugs. Additionally, we have collected a feature selection dataset from Muehlbacher et al. (J Comput Aided Mol Des 25(12):1095-1106, 2011. 10.1007/s10822-011-9478-1) and an in-house dataset. This information was utilised to design a molecular fingerprint that was used to train machine learning (ML) models. The best-performing models reported in this study achieved accuracies of 0.997 and 0.98, sensitivities of 1.0 and 0.992, specificities of 0.971 and 0.962, MCCs of 0.984 and 0.958, and ROC-AUCs of 0.997 and 0.999 on an imbalanced and a balanced dataset, respectively. They demonstrated overall good accuracies and sensitivities in the blind validation dataset. The reported models can be applied for fast and early screening drug-like molecules with BBB potential. Furthermore, the bbbPythoN package can be used by the research community to both produce the BBB-specific molecular fingerprints and employ the models mentioned earlier for BBB-permeability prediction.
摘要：
与中枢神经系统（CNS）相关的疾病是主要的健康问题，并具有严重的社会和经济影响。开发用于CNS相关病症的新药提出了重大挑战,因为其积极地涉及将药物递送到CNS中。因此,必须开发计算机方法，以可靠地识别可以穿透血脑屏障（BBB）的潜在铅化合物，并帮助彻底了解不同的物理化学性质对分子的BBB渗透至关重要。在这项研究中,我们分析了CNS药物的化学空间,并将其与未经CNS批准的药物进行了比较.此外,我们从Muehlbacher等人那里收集了一个特征选择数据集。(J计算辅助MolDes25(12)：1095-1106，2011。10.1007/s10822-011-9478-1)和内部数据集。这些信息用于设计用于训练机器学习（ML）模型的分子指纹。在不平衡和平衡的数据集上，本研究报告的最佳性能模型的准确度为0.997和0.98，灵敏度为1.0和0.992，特异性为0.971和0.962，MCCs为0.984和0.958，ROC-AUC为0.997和0.999，分别。他们在盲验证数据集中表现出整体良好的准确性和敏感性。报道的模型可用于快速和早期筛选具有BBB潜力的药物样分子。此外，研究社区可以使用bbbPythoN包产生BBB特异性分子指纹，并采用前面提到的模型进行BBB渗透率预测。
