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Rcpi: R/Bioconductor package to generate various descriptors of proteins, compounds and their interactions

机译:Rcpi:R / Bioconductor程序包,用于生成蛋白质,化合物及其相互作用的各种描述符

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摘要

In chemoinformatics and bioinformatics fields, one of the main computational challenges in various predictive modeling is to find a suitable way to effectively represent the molecules under investigation, such as small molecules, proteins and even complex interactions. To solve this problem, we developed a freely available R/Bioconductor package, called Compound-Protein Interaction with R (Rcpi), for complex molecular representation from drugs, proteins and more complex interactions, including protein-protein and compound-protein interactions. Rcpi could calculate a large number of structural and physicochemical features of proteins and peptides from amino acid sequences, molecular descriptors of small molecules from their topology and protein-protein interaction and compound-protein interaction descriptors. In addition to main functionalities, Rcpi could also provide a number of useful auxiliary utilities to facilitate the user's need. With the descriptors calculated by this package, the users could conveniently apply various statistical machine learning methods in R to solve various biological and drug research questions in computational biology and drug discovery.
机译:在化学信息学和生物信息学领域,各种预测模型中的主要计算挑战之一是找到一种合适的方法来有效表示所研究的分子,例如小分子,蛋白质甚至复杂的相互作用。为了解决这个问题,我们开发了一种免费的R / Bioconductor程序包,称为与R的蛋白质-蛋白质相互作用(Rcpi),用于药物,蛋白质的复杂分子表示以及更复杂的相互作用,包括蛋白质-蛋白质和化合物-蛋白质的相互作用。 Rcpi可以从氨基酸序列,小分子的分子结构,拓扑结构,蛋白质-蛋白质相互作用和化合物-蛋白质相互作用描述子中计算蛋白质和肽的大量结构和理化特征。除了主要功能外,Rcpi还可以提供许多有用的辅助实用程序来满足用户的需求。使用此程序包计算的描述符,用户可以方便地在R中应用各种统计机器学习方法,以解决计算生物学和药物发现中的各种生物学和药物研究问题。

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