神经系统疾病药物开发中的机器学习:血脑屏障渗透性预测模型综述。

IF 2.8 4区 医学 Q3 CHEMISTRY, MEDICINAL
Aryon Eckleel Nabi, Pedram Pouladvand, Litian Liu, Ning Hua, Cyrus Ayubcha
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引用次数: 0

摘要

血脑屏障(BBB)是一种内皮衍生的结构,它限制了一般躯体循环系统与中枢神经系统(CNS)之间某些分子的运动。虽然血脑屏障通过调节脑血管灌注诱导的分子环境来维持体内平衡,但在开发针对中枢神经系统靶点的治疗方法方面也面临着重大挑战。许多药物开发实践部分依赖于广泛的细胞和动物模型来预测,在一定程度上,未来的治疗分子是否可以穿过血脑屏障。为了降低成本和提高预测精度,许多人建议利用经验数据对血脑屏障渗透率剖面进行先进的计算建模。鉴于机器学习和深度学习的增长规模,我们回顾了预测血脑屏障渗透率的最新机器学习方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning in Drug Development for Neurological Diseases: A Review of Blood Brain Barrier Permeability Prediction Models.

The blood brain barrier (BBB) is an endothelial-derived structure which restricts the movement of certain molecules between the general somatic circulatory system to the central nervous system (CNS). While the BBB maintains homeostasis by regulating the molecular environment induced by cerebrovascular perfusion, it also presents significant challenges in developing therapeutics intended to act on CNS targets. Many drug development practices rely partly on extensive cell and animal models to predict, to an extent, whether prospective therapeutic molecules can cross the BBB. In interest to reduce costs and improve prediction accuracy, many propose using advanced computational modeling of BBB permeability profiles leveraging empirical data. Given the scale of growth in machine learning and deep learning, we review the most recent machine learning approaches in predicting BBB permeability.

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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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