Ekins Sean, Clark Alex M, Swamidass S Joshua, Litterman Nadia, Williams Antony J
Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC, 27526, USA,
J Comput Aided Mol Des. 2014 Oct;28(10):997-1008. doi: 10.1007/s10822-014-9762-y. Epub 2014 Jun 19.
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 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.
在过去十年中，我们看到化学数据和化学信息学工具的提供有所增长，它们以免费网站或软件即服务的商业产品形式存在。这些改变了我们在研究中查找与分子相关数据以及使用此类工具的方式。也有人努力通过公开或使用商业工具进行安全交易来改善研究人员之间的合作。未来的一个主要挑战将是此类数据库和软件方法如何处理随着高通量筛选积累而来的大量数据，并使用户能够得出见解、进行预测并推动项目进展。我们现在讨论如何使一些药物发现数据集的信息更易于获取，以及数据隐私如何不应掩盖在适当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，并阐述了我们对大数据时代预测性药物发现未来的看法。我们使用来自我们自己在被忽视疾病研究、合作、移动应用和算法开发方面的一些例子来说明这些观点。
