Abstract
Big data brings new ways of data-driven drug research and development, which are expected to prominently increase the success rate, shorten the time and reduce the cost of drug discovery. In this review, we briefly summarized the development of data integration and data mining in drug design and drug information in recent years. Semantic web with techniques such as resource description framework, web ontology language, simple protocol and RDF query language, and linked data were commonly used for drug related data integration. Machine learning methods such as support vector machine and artificial neural network were widely used for data mining in drug information processing. We also give an outlook on future data integration and data mining for drug design and drug information. The rapidly booming big data integration and processing techniques can be adopted and a new machine learning method, deep learning, is specially recommended for data mining in the field of drug design and drug information.
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