Novel opportunities for computational biology and sociology in drug discovery: Corrected paper
作者： Lixia Yao , James A. Evans , Andrey Rzhetsky
DOI: 10.1016/J.TIBTECH.2010.01.004
关键词: Complement (complexity) 、 Process (engineering) 、 Computational biology 、 Drug discovery
摘要: Current drug discovery is impossible without sophisticated modeling and computation. In this review we outline previous advances in computational biology and, by tracing the steps involved pharmaceutical development, explore a range of novel, high-value opportunities for innovation biological process disease social discovery. These include text mining new leads, molecular pathways predicting efficacy cocktails, analyzing genetic overlap between diseases alternative use. Computation can also be used to model research teams innovative regions estimate value academy–industry links scientific human benefit. Attention these could promise punctuated advance will complement well-established work on which currently relies.
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