Ben Miled Zina, Liu Yang, Powers David, Bukhres Omran, Bem Michael, Jones Robert, Oppelt Robert J
Electrical Engineering Department, Purdue School of Engineering & Technology, Indianapolis, Indiana 46202, USA.
J Chem Inf Comput Sci. 2003 Jan-Feb;43(1):25-35. doi: 10.1021/ci0255275.
The recent advances in laboratory technologies have resulted in a wealth of chemical and biological data. The rapid proliferation of a vast amount of data has led to a set of cheminformatics and bioinformatics applications that manipulate dynamic, heterogeneous, and massive data. An example of such application in the pharmaceutical industry is the computational process involved in the early discovery of lead drug candidates for a given target disease. In this paper, an efficient implementation of a drug candidate database is presented and evaluated. This study shows that high performance data access can be achieved through proper choices of data representation, database schema design, and parallel processing techniques.
实验室技术的最新进展产生了大量的化学和生物学数据。大量数据的迅速激增催生了一系列化学信息学和生物信息学应用程序,这些程序可处理动态、异构和海量数据。制药行业中此类应用的一个例子是针对给定目标疾病早期发现潜在先导药物所涉及的计算过程。本文介绍并评估了一种高效的候选药物数据库实现方法。这项研究表明,通过正确选择数据表示、数据库模式设计和并行处理技术,可以实现高性能的数据访问。