ChemVassa: A New Method for Identifying Small Molecule Hits in Drug Discovery.

Q2 Pharmacology, Toxicology and Pharmaceutics Open Medicinal Chemistry Journal Pub Date : 2012-01-01 Epub Date: 2012-11-30 DOI:10.2174/1874104501206010029
Brian Moldover, Ada Solidar, Christa Montgomery, Henry Miziorko, Jeff Murphy, Gerald J Wyckoff
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引用次数: 4

Abstract

ChemVassa, a new chemical structure search technology, was developed to allow rapid in silico screening of compounds for hit and hit-to-lead identification in drug development. It functions by using a novel type of molecular descriptor that examines, in part, the structure of the small molecule undergoing analysis, yielding its "information signature." This descriptor takes into account the atoms, bonds, and their positions in 3-dimensional space. For the present study, a database of ChemVassa molecular descriptors was generated for nearly 16 million compounds (from the ZINC database and other compound sources), then an algorithm was developed that allows rapid similarity searching of the database using a query molecular descriptor (e.g., the signature of atorvastatin, below). A scoring metric then allowed ranking of the search results. We used these tools to search a subset of drug-like molecules using the signature of a commercially successful statin, atorvastatin (Lipitor™). The search identified ten novel compounds, two of which have been demonstrated to interact with HMG-CoA reductase, the macromolecular target of atorvastatin. In particular, one compound discussed in the results section tested successfully with an IC50 of less than 100uM and a completely novel structure relative to known inhibitors. Interactions were validated using computational molecular docking and an Hmg-CoA reductase activity assay. The rapidity and low cost of the methodology, and the novel structure of the interactors, suggests this is a highly favorable new method for hit generation.

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ChemVassa:在药物发现中识别小分子靶点的新方法。
ChemVassa是一种新的化学结构搜索技术,用于在药物开发中快速筛选化合物,以进行命中和命中-先导识别。它通过使用一种新型的分子描述符来发挥作用,这种描述符部分地检查正在分析的小分子的结构,从而产生其“信息签名”。这个描述符考虑了原子、键和它们在三维空间中的位置。在本研究中,为近1600万种化合物(来自ZINC数据库和其他化合物来源)生成了ChemVassa分子描述符数据库,然后开发了一种算法,允许使用查询分子描述符对数据库进行快速相似性搜索(例如,下图中的阿托伐他汀的签名)。然后,评分指标允许对搜索结果进行排名。我们使用这些工具搜索一个药物样分子子集,使用商业上成功的他汀类药物阿托伐他汀(立普妥™)的特征。研究发现了10种新化合物,其中两种已被证明与HMG-CoA还原酶相互作用,HMG-CoA还原酶是阿托伐他汀的大分子靶标。特别是,结果部分中讨论的一种化合物成功地测试了IC50小于100uM,并且相对于已知抑制剂具有全新的结构。通过计算分子对接和Hmg-CoA还原酶活性测定来验证相互作用。该方法的快速和低成本以及交互器的新颖结构表明,这是一种非常有利的新方法。
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来源期刊
Open Medicinal Chemistry Journal
Open Medicinal Chemistry Journal Pharmacology, Toxicology and Pharmaceutics-Pharmaceutical Science
CiteScore
4.40
自引率
0.00%
发文量
4
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