用计算机方法发现治疗阿尔茨海默病的多靶点定向配体

Q2 Medicine In Silico Biology Pub Date : 2010-02-15 DOI:10.1145/1722024.1722032

A. Tyagi, Shikha Gupta, C. G. Mohan

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引用次数: 0

摘要

多靶点定向(MTD)药物已被发现在控制神经退行性疾病方面非常有效。我们已经开发了一种硅策略来筛选AChE和BACE-1酶双重抑制的分子。利用已知AChE和BACE-1抑制剂的药效团模型，对三种不同的小分子数据库进行序贯虚拟筛选(VS)。使用这些序列VS技术鉴定了8个新的MTD配体。根据Gold对接评分和Log-BB值，从NCI数据库中获得的分子2是最有希望命中的，可以进一步进行实验分析。我们目前鉴定AChE和BACE-1双抑制剂的策略可能是发现治疗阿尔茨海默病的更好线索的有希望的方向之一。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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In silico approach to discover multi-target-directed ligands for the treatment of Alzheimer's disease

Multi-target directed (MTD) drugs have been found to be very effective in controlling neurodegenerative diseases. We have developed an in silico strategy to screen molecules for both AChE and BACE-1 enzyme dual inhibition. Pharmacophore model development of known AChE and BACE-1 inhibitors were used for sequential virtual screening (VS) of three different small molecule databases. Eight new MTD ligands were identified using these sequential VS techniques. Among these molecule 2 obtained from NCI database was found to be most promising hit on the basis of Gold docking score and Log-BB value, and which could be further explored for experimental analysis. Our present strategy for identification of the AChE and BACE-1 dual inhibitors might be one of the promising directions to discover better leads for the treatment of Alzheimer's disease.

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来源期刊

In Silico Biology Computer Science-Computational Theory and Mathematics

CiteScore

2.20

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0.00%

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期刊介绍： The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.