基于5-芳基乙内酰脲类化合物的CoMFA和HQSAR研究

用CoMFA和HQSAR两种QSAR方法研究了50个乙内酰脲类分子的定量构效关系.本研究从构象搜索所得的低能结构出发构建化合物分子的构象, 建立CoMFA模型,并进行了全空间搜索. HQSAR本质上是一种二维的QSAR方法,与CoMFA方法相比,该方法在数据处理方面,比CoMFA方法快捷,并且可重复性好.两种方法均得到了较好分析结果, CoMFA的交叉验证相关系数q2 值为0.815, HQSAR的q2值为0.893.这些方程有力地说明了该类分子在(R,R)-N-3,5-dinitrobenzoyl-1,2-diamine型手性固定相上拆分过程中的影响因素,对今后类似拆分的实验研究提供了理论支持.     

Developing an antituberculosis compounds database and data mining in the search of a motif responsible for the activity of a diverse class of antituberculosis agents

A novel data mining procedure to look for new antitubercular agents and targets as well as to find a minimum common bioactive substructure (MCBS), has been reported here. The methodology extracts MCBS, both across the diverse chemical classes and within the particular chemical class, known to be present in the various marketed drugs alongside antimycobacterial compounds with known MICs. For this purpose a small in-house database of compounds has been created, for which MICs against Mycobacterium are known. The compounds have been collected from literature available on the synthetic compounds, having known MICs against Mycobacterium tuberculosis. An elaborate HQSAR (Hologram QSAR) study has been attempted to extract active fragment from a diverse class of compounds, in combination with the clustering technique to select a homogeneous group of compounds having good a profile toward the activity. The 2D pharmacophore (the 2D fragments extracted from HQSAR) has been validated searching the database. It has been found further that this validated 2D pharmacophore could be used for searching the orphan target in Mycobacterium effectively.