Bigger data, collaborative tools and the future of predictive drug discovery.

Bigger data, collaborative tools and the future of predictive drug discovery.
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DOI:
10.1007/s10822-014-9762-y
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发表时间:
2014-10
影响因子:
3.5
通讯作者:
Williams, Antony J.
Williams, Antony J.
中科院分区:
生物学3区
文献类型:
--
作者:
Ekins, Sean;Clark, Alex M.;Swamidass, S. Joshua;Litterman, Nadia;Williams, Antony J.

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在过去的十年中,我们已经看到了化学数据和化学信息学工具作为免费网站或软件即服务(SaaS)商业产品的增长。这些改变了我们如何找到分子相关的数据,并在我们的研究中使用这些工具。此外,还努力改善研究人员之间的合作,无论是公开的还是通过使用商业工具的安全交易。未来的一个主要挑战将是这些数据库和软件方法如何处理从高通量筛选中积累的大量数据,并使用户能够获得见解,实现预测并推动项目向前发展。我们现在讨论如何使来自一些药物发现数据集的信息更容易访问,以及数据的隐私如何不应压倒在适当的时候与合作者共享数据的愿望。我们还讨论了可以提供的其他软件工具,并提供了我们对大数据时代预测药物发现未来的想法。我们使用我们自己在被忽视的疾病、合作、移动的应用程序和算法开发方面的研究中的一些例子来说明这些想法。
Over the past decade we have seen a growth in the provision of chemistry data and cheminformatics tools as either free websites or software as a service (SaaS) commercial offerings. These have transformed how we find molecule-related data and use such tools in our research. There have also been efforts to improve collaboration between researchers either openly or through secure transactions using commercial tools. A major challenge in the future will be how such databases and software approaches handle larger amounts of data as it accumulates from high throughput screening and enables the user to draw insights, enable predictions and move projects forward. We now discuss how information from some drug discovery datasets can be made more accessible and how privacy of data should not overwhelm the desire to share it at an appropriate time with collaborators. We also discuss additional software tools that could be made available and provide our thoughts on the future of predictive drug discovery in this age of big data. We use some examples from our own research on neglected diseases, collaborations, mobile apps and algorithm development to illustrate these ideas.
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