摘要
多靶点药物对特定多靶点具有较好的保护性、抵抗力和治疗作用,其关键靶点是具有抵抗行为和代偿因素的合作规则。计算技术可以帮助我们在早期阶段努力设计新的药物(配体)的首选生物活性轮廓和替代生物活性分子。一批在硅片的方法已被广泛的探索以促进个人目标代理商的调查提出了一个选择性的药物。一个不同的,逐步更重要的领域是用来预测生物活性化合物的数据挖掘方法。前面提到的一些方法已经研究了多靶点药物设计(医疗损害纠纷)发现药物导致的相互作用同时与多个目标。一些化学信息学的方法和基于结构的方法来提取信息,从单位合作在生物系统来完成他们的任务。占主导地位的配体的目标结构,实验规范的困境的合理方法,即分子对接,SAR和QSAR是获得知识对每个结构中原子的洞察力至关重要的替代品。这些程序在逻辑上是成功的结合亲和力的预测和促进医疗损害纠纷有良好的前景。在这里,我们回顾了一些使用的计算方法的多靶点治疗药物发现的重要特征,突出了SAR,QSAR,对接和药效的方法发现的药物靶标之间的相互作用可以用于治疗的好处。每一个总结,其次是在药物设计中的应用实例。每个方法的计算效率已表示根据其主要优点和局限性。
关键词: 多靶点、药物设计、电子、SAR、构效关系、分子对接。
图形摘要
Current Drug Targets
Title:Computer Aided Drug Design for Multi-Target Drug Design: SAR /QSAR, Molecular Docking and Pharmacophore Methods
Volume: 18 Issue: 5
关键词: 多靶点、药物设计、电子、SAR、构效关系、分子对接。
摘要: Multi-target drugs against particular multiple targets get better protection, resistance profiles and curative influence by cooperative rules of a key beneficial target with resistance behavior and compensatory elements. Computational techniques can assist us in the efforts to design novel drugs (ligands) with a preferred bioactivity outline and alternative bioactive molecules at an early stage. A number of in silico methods have been explored extensively in order to facilitate the investigation of individual target agents and to propose a selective drug. A different, progressively more significant field which is used to predict the bioactivity of chemical compounds is the data mining method. Some of the previously mentioned methods have been investigated for multi-target drug design (MTDD) to find drug leads interact simultaneously with multiple targets. Several cheminformatics methods and structure-based approaches try to extract information from units working cooperatively in a biomolecular system to fulfill their task. To dominate the difficulties of the experimental specification of ligand-target structures, rational methods, namely molecular docking, SAR and QSAR are vital substitutes to obtain knowledge for each structure in atomic insight. These procedures are logically successful for the prediction of binding affinity and have shown promising potential in facilitating MTDD. Here, we review some of the important features of the multi-target therapeutics discoveries using the computational approach, highlighting the SAR, QSAR, docking and pharmacophore methods to discover interactions between drug-target that could be leveraged for curative benefits. A summary of each, followed by examples of its applications in drug design has been provided. Computational efficiency of each method has been represented according to its main strengths and limitations.
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Cite this article as:
Computer Aided Drug Design for Multi-Target Drug Design: SAR /QSAR, Molecular Docking and Pharmacophore Methods, Current Drug Targets 2017; 18 (5) . https://dx.doi.org/10.2174/1389450117666160101120822
DOI https://dx.doi.org/10.2174/1389450117666160101120822 |
Print ISSN 1389-4501 |
Publisher Name Bentham Science Publisher |
Online ISSN 1873-5592 |
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