Prediction of blood-brain barrier permeability using machine learning approaches based on various molecular representation.

IF 2.8 4区 医学 Q3 CHEMISTRY, MEDICINAL
Molecular Informatics Pub Date : 2024-09-01 Epub Date: 2024-06-12 DOI:10.1002/minf.202300327
Li Liang, Zhiwen Liu, Xinyi Yang, Yanmin Zhang, Haichun Liu, Yadong Chen
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引用次数: 0

Abstract

The assessment of compound blood-brain barrier (BBB) permeability poses a significant challenge in the discovery of drugs targeting the central nervous system. Conventional experimental approaches to measure BBB permeability are labor-intensive, cost-ineffective, and time-consuming. In this study, we constructed six machine learning classification models by combining various machine learning algorithms and molecular representations. The model based on ExtraTree algorithm and random partitioning strategy obtains the best prediction result, with AUC value of 0.932±0.004 and balanced accuracy (BA) of 0.837±0.010 for the test set. We employed the SHAP method to identify important features associated with BBB permeability. In addition, matched molecular pair (MMP) analysis and representative substructure derivation method were utilized to uncover the transformation rules and distinctive structural features of BBB permeable compounds. The machine learning models proposed in this work can serve as an effective tool for assessing BBB permeability in the drug discovery for central nervous system disease.

利用基于各种分子表征的机器学习方法预测血脑屏障通透性。
化合物血脑屏障(BBB)通透性评估是发现中枢神经系统靶向药物的一大挑战。测量血脑屏障通透性的传统实验方法耗费大量人力、成本低且费时。在本研究中,我们结合各种机器学习算法和分子表征,构建了六个机器学习分类模型。基于 ExtraTree 算法和随机分区策略的模型获得了最佳预测结果,其 AUC 值为 0.932±0.004,测试集的平衡准确度(BA)为 0.837±0.010。我们采用 SHAP 方法来识别与 BBB 渗透性相关的重要特征。此外,我们还利用匹配分子对(MMP)分析法和代表性子结构推导法来揭示BBB渗透性化合物的转化规则和独特的结构特征。本研究提出的机器学习模型可作为评估中枢神经系统疾病药物研发中BBB渗透性的有效工具。
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来源期刊
Molecular Informatics
Molecular Informatics CHEMISTRY, MEDICINAL-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.30
自引率
2.80%
发文量
70
审稿时长
3 months
期刊介绍: Molecular Informatics is a peer-reviewed, international forum for publication of high-quality, interdisciplinary research on all molecular aspects of bio/cheminformatics and computer-assisted molecular design. Molecular Informatics succeeded QSAR & Combinatorial Science in 2010. Molecular Informatics presents methodological innovations that will lead to a deeper understanding of ligand-receptor interactions, macromolecular complexes, molecular networks, design concepts and processes that demonstrate how ideas and design concepts lead to molecules with a desired structure or function, preferably including experimental validation. The journal''s scope includes but is not limited to the fields of drug discovery and chemical biology, protein and nucleic acid engineering and design, the design of nanomolecular structures, strategies for modeling of macromolecular assemblies, molecular networks and systems, pharmaco- and chemogenomics, computer-assisted screening strategies, as well as novel technologies for the de novo design of biologically active molecules. As a unique feature Molecular Informatics publishes so-called "Methods Corner" review-type articles which feature important technological concepts and advances within the scope of the journal.
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