Nathan Brown, Jean Cambruzzi, Peter J Cox, Mark Davies, James Dunbar, Dean Plumbley, Matthew A Sellwood, Aaron Sim, Bryn I Williams-Jones, Magdalena Zwierzyna, David W Sheppard
{"title":"药物发现中的大数据。","authors":"Nathan Brown, Jean Cambruzzi, Peter J Cox, Mark Davies, James Dunbar, Dean Plumbley, Matthew A Sellwood, Aaron Sim, Bryn I Williams-Jones, Magdalena Zwierzyna, David W Sheppard","doi":"10.1016/bs.pmch.2017.12.003","DOIUrl":null,"url":null,"abstract":"<p><p>Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation.</p>","PeriodicalId":20755,"journal":{"name":"Progress in medicinal chemistry","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/bs.pmch.2017.12.003","citationCount":"33","resultStr":"{\"title\":\"Big Data in Drug Discovery.\",\"authors\":\"Nathan Brown, Jean Cambruzzi, Peter J Cox, Mark Davies, James Dunbar, Dean Plumbley, Matthew A Sellwood, Aaron Sim, Bryn I Williams-Jones, Magdalena Zwierzyna, David W Sheppard\",\"doi\":\"10.1016/bs.pmch.2017.12.003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation.</p>\",\"PeriodicalId\":20755,\"journal\":{\"name\":\"Progress in medicinal chemistry\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/bs.pmch.2017.12.003\",\"citationCount\":\"33\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Progress in medicinal chemistry\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/bs.pmch.2017.12.003\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2018/2/24 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"Pharmacology, Toxicology and Pharmaceutics\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Progress in medicinal chemistry","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/bs.pmch.2017.12.003","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2018/2/24 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"Pharmacology, Toxicology and Pharmaceutics","Score":null,"Total":0}
Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation.
期刊介绍:
This series has a long established reputation for excellent coverage of almost every facet of Medicinal Chemistry and is one of the most respected and instructive sources of information on the subject. The latest volume certifies to the continuing success of a unique series reflecting current progress in a broadly developing field of science.