药物分子设计中的大数据问题  

Big data in drug design

作　　者：严鑫[1] 丁鹏[1] 刘志红[1] 王领[1] 廖晨钟[2] 顾琼[1] 徐峻[1,2] 

机构地区：[1]中山大学药学院药物分子设计与生物超算中心,广州510006 [2]合肥工业大学医学工程学院,合肥230009

出　　处：《科学通报》2015年第5期558-565,共8页Chinese Science Bulletin

基　　金：国家自然科学基金(81173470);国家高技术研究发展计划(2012AA020307);广东省引进创新科研团队专项计划(2009010058);广州超级计算应用研发与扶持专项(2012Y2-00048);中央高校基本科研业务费专项(2013HGCH0015)资助

摘　　要：药物创新领域的大数据主要来源于高通量实验、高效能模拟计算、信息化、科技出版物和专利文献4个方面.这些大数据使我们有可能在系统层面上看到药物分子与许多靶标相互作用的新现象、新规律,提高药物创新的效率,也带来新的挑战,如存储、标引/标注和质控、可视化、数据挖掘和计算复杂度等问题.这些问题可以通过在超算和云服务技术的支持下发展并行计算方法而逐渐得到解决.从离散、不完备且信噪比低的大数据中难以找到物质活性与结构之间的连续函数关系,贝叶斯学习机及其与支持向量机、决策树技术的组合是大数据挖掘的发展方向.大数据既是科学实验通量化和社会信息化的结果又是原因,正确解决大数据挖掘问题是提高药物创新效率的核心.Big data collection in the pharmaceutical research industry has four sources, high-throughput scientific experiments, high-performance computations, automated information acquisition and office automation, and scientific publications and patents. Big data is the product and the promoter of high-performance scientific experiments, therefore the technology for mining big data is the key to future drug discovery. However, big data brings greater challenges, such as, storage, retrieval, curation and quality assurance, sharing/transfer, analysis, visualization, modeling and computing complexities. This review outlines the current progress of processing big data in the drug design field. These problems may be resolved by adopting cloud computing and high-performance computing technologies, and parallelizing existing chemoinformatics and bioinformatics programs. Machine-learning approaches involving Bayesian learning methods and other methods, such as support vector machine and recursive petitioning, can be used for big data mining. Recent progress includes parallelized and GPU-accelerated molecular dynamics simulation technology, enhanced molecular docking technology, new parallelized algorithms for shape-based virtual screening, free-energy landscape calculations, and machine-learning algorithms for big chemical structural data mining. Big data from drug discoveries will increase, so conventional drug design software and methods need to be upgraded. This is a long-term project and we highlight the tasks that need to be accomplished to meet this goal.

关 键 词：大数据 药物设计 生物信息学 化学信息学 高性能计算 

分 类 号：R91[医药卫生—药学] TP311.13[自动化与计算机技术—计算机软件与理论]