Bigger data, collaborative tools and the future of predictive drug discovery
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...
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| Veröffentlicht in: | Journal of computer-aided molecular design 2014-10, Vol.28 (10), p.997-1008 |
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| container_title | Journal of computer-aided molecular design |
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| creator | Ekins, Sean Clark, Alex M. Swamidass, S. Joshua Litterman, Nadia Williams, Antony J. |
| description | 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. |
| doi_str_mv | 10.1007/s10822-014-9762-y |
| format | Article |
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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. 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Joshua</creatorcontrib><creatorcontrib>Litterman, Nadia</creatorcontrib><creatorcontrib>Williams, Antony J.</creatorcontrib><title>Bigger data, collaborative tools and the future of predictive drug discovery</title><title>Journal of computer-aided molecular design</title><addtitle>J Comput Aided Mol Des</addtitle><addtitle>J Comput Aided Mol Des</addtitle><description>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. 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| ispartof | Journal of computer-aided molecular design, 2014-10, Vol.28 (10), p.997-1008 |
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| source | MEDLINE; Springer Nature - Complete Springer Journals |
| subjects | Accessibility Animal Anatomy Applications programs Chemistry Chemistry and Materials Science Computer aided design Computer Applications in Chemistry Computer programs Cooperative Behavior Databases, Factual Drug Discovery - methods Drugs Histology Humans Information management Mobile communication systems Morphology Pharmaceutical sciences Physical Chemistry Quantitative Structure-Activity Relationship Rare Diseases Software Software-as-a-service Statistics as Topic - methods |
| title | Bigger data, collaborative tools and the future of predictive drug discovery |
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