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具有抗惊厥活性的化合物的定量构效关系模型。

Quantitative structure-activity relationship models for compounds with anticonvulsant activity.

机构信息

a Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences , University of La Plata (UNLP) , La Plata, Buenos Aires , Argentina.

b CCT La Plata , Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) , Buenos Aires , Argentina.

出版信息

Expert Opin Drug Discov. 2019 Jul;14(7):653-665. doi: 10.1080/17460441.2019.1613368. Epub 2019 May 10.

Abstract

: Third-generation antiepileptic drugs have seemingly failed to improve the global figures of seizure control and can still be regarded as symptomatic treatments. Quantitative structure-activity relationships (QSAR) can be used to guide hit-to-lead and lead optimization projects and applied to the large-scale virtual screening of chemical libraries. : In this review, the authors cover reports on QSAR models related to antiepileptic drugs and drug targets in epilepsy, analyzing whether they refer to classic or non-classic QSAR and if they apply QSAR as a descriptive or predictive approach, among other considerations. The article finally focuses on a more detailed discussion of those predictive studies which include some sort of experimental validation, i.e. papers in which the reported models have been used to identify novel active compounds which have been tested in vitro and/or in vivo. : There are significant opportunities to apply the QSAR methodology to assist the discovery of more efficacious antiepileptic drugs. Considering the intrinsic complexity of the disorder, such applications should focus on state-of-the-art approximations (e.g. systemic, multi-target and multi-scale QSAR as well as ensemble and deep learning) and modeling the effects on novel drug targets and modern screening tools.

摘要

第三代抗癫痫药物似乎未能改善全球的癫痫控制数据,仍可被视为对症治疗。定量构效关系(QSAR)可用于指导苗头化合物到先导化合物优化项目,并应用于化学库的大规模虚拟筛选。

在这篇综述中,作者涵盖了与癫痫中的抗癫痫药物和药物靶点相关的 QSAR 模型的报告,分析了它们是否涉及经典或非经典 QSAR,以及它们是否将 QSAR 作为描述性或预测性方法应用,以及其他考虑因素。文章最后重点讨论了那些具有某种实验验证的预测性研究,即报告的模型已被用于鉴定已在体外和/或体内测试的新型活性化合物的论文。

有很大的机会应用 QSAR 方法学来帮助发现更有效的抗癫痫药物。考虑到该疾病的内在复杂性,此类应用应侧重于最先进的近似值(例如系统、多靶标和多尺度 QSAR 以及集成和深度学习)以及对新型药物靶点和现代筛选工具的效果建模。

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