• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Pacific Symposium on Biocomputing >COMPARATIVE QSAR ANALYSIS OF BACTERIAL, FUNGAL PLANT AND HUMAN METABOLITES
【24h】

COMPARATIVE QSAR ANALYSIS OF BACTERIAL, FUNGAL PLANT AND HUMAN METABOLITES

机译：细菌，真菌植物和人代代谢物的比较QSAR分析

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Several QSAR models have been developed using a linear optimization approach that enabled distinguishing metabolic substances isolated from human-, bacterial-, plant- and fungal- cells. Seven binary classifiers based on a k-Nearest Neighbors method have been created using a variety of 'inductive' and traditional QSAR descriptors that allowed up to 95% accurate recognition of the studied groups of chemical substances. The conducted comparative QSAR analysis based on the above mentioned linear optimization approach helped to identify the extent of overlaps between the groups of compounds, such as cross-recognition of fungal and bacterial metabolites and association between fungal and plant substances. Human metabolites exhibited very different QSAR behavior in chemical space and demonstrated no significant overlap with bacterial-, fungal-, and plant-derived molecules. When the developed QSAR models were applied to collections of conventional human therapeutics and antimicrobials, it was observed that the first group of substances demonstrate the strongest association with human metabolites, while the second group exhibit tendency of 'bacterial metabolite - like' behavior. We speculate that the established 'drugs - human metabolites' and 'antimicrobials - bacterial metabolites' associations result from strict bioavailability requirements imposed on conventional therapeutic substances, which further support their metabolite-like properties. It is anticipated that the study may bring additional insight into QSAR determinants for human-, bacterial-, fungal- and plant metabolites and may help rationalizing design and discovery of novel bioactive substances with improved, metabolite-like properties.
机译：使用了使用线性优化方法开发了几种QSAR模型，使能与人，细菌，植物和真菌细胞分离的代谢物质区分。已经使用各种“电感”和传统的QSAR描述符创建了基于K-Collect邻居方法的七个二进制分类器，其允许高达95％的化学物质组准确识别。基于上述线性优化方法的进行的对比QSAR分析有助于确定化合物组之间的重叠程度，例如对真菌和细菌代谢物之间的交叉识别和真菌和植物物质之间的关联。人的代谢产物在化学空间中表现出非常不同的QSAR行为，并表现出与细菌，真菌和植物衍生的分子没有显着重叠。当发育的QSAR模型被应用于常规人类治疗和抗微生物的收集时，观察到第一组物质表明与人代谢物最强，而第二组表现出“细菌代谢物类似”行为的趋势。我们推测，已建立的“药物 - 人代代谢物”和“抗菌药物 - 细菌代谢物”关联由严格对常规治疗物质的严格生物利用度要求引起，这进一步支持其代谢物状性质。预计该研究可能会对人，细菌，真菌和植物代谢物的QSAR决定因素提供额外的洞察力，并且可以帮助合理化新的生物活性物质的设计和发现具有改善的代谢物状性质。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号