Literature DB >> 12546870

Discovery informatics: its evolving role in drug discovery.

Brian L Claus1, Dennis J Underwood.   

Abstract

Drug discovery and development is a highly complex process requiring the generation of very large amounts of data and information. Currently this is a largely unmet informatics challenge. The current approaches to building information and knowledge from large amounts of data has been addressed in cases where the types of data are largely homogeneous or at the very least well-defined. However, we are on the verge of an exciting new era of drug discovery informatics in which methods and approaches dealing with creating knowledge from information and information from data are undergoing a paradigm shift. The needs of this industry are clear: Large amounts of data are generated using a variety of innovative technologies and the limiting step is accessing, searching and integrating this data. Moreover, the tendency is to move crucial development decisions earlier in the discovery process. It is crucial to address these issues with all of the data at hand, not only from current projects but also from previous attempts at drug development. What is the future of drug discovery informatics? Inevitably, the integration of heterogeneous, distributed data are required. Mining and integration of domain specific information such as chemical and genomic data will continue to develop. Management and searching of textual, graphical and undefined data that are currently difficult, will become an integral part of data searching and an essential component of building information- and knowledge-bases.

Mesh:

Year:  2002        PMID: 12546870     DOI: 10.1016/s1359-6446(02)02433-9

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  6 in total

1.  Non-stochastic and stochastic linear indices of the molecular pseudograph's atom-adjacency matrix: a novel approach for computational in silico screening and "rational" selection of new lead antibacterial agents.

Authors:  Yovani Marrero-Ponce; Ricardo Medina Marrero; Francisco Torrens; Yamile Martinez; Milagros García Bernal; Vicente Romero Zaldivar; Eduardo A Castro; Ricardo Grau Abalo
Journal:  J Mol Model       Date:  2005-11-04       Impact factor: 1.810

2.  ADAAPT: Amgen's data access, analysis, and prediction tools.

Authors:  Sung Jin Cho; Yaxiong Sun; William Harte
Journal:  J Comput Aided Mol Des       Date:  2006-06-21       Impact factor: 3.686

3.  Discovering active compounds from mixture of natural products by data mining approach.

Authors:  Yi Wang; Yecheng Jin; Chenguang Zhou; Haibin Qu; Yiyu Cheng
Journal:  Med Biol Eng Comput       Date:  2008-03-05       Impact factor: 2.602

4.  CYANOS: a data management system for natural product drug discovery efforts using cultured microorganisms.

Authors:  George E Chlipala; Aleksej Krunic; Shunyan Mo; Megan Sturdy; Jimmy Orjala
Journal:  J Chem Inf Model       Date:  2010-12-16       Impact factor: 4.956

5.  Brunn: an open source laboratory information system for microplates with a graphical plate layout design process.

Authors:  Jonathan Alvarsson; Claes Andersson; Ola Spjuth; Rolf Larsson; Jarl E S Wikberg
Journal:  BMC Bioinformatics       Date:  2011-05-20       Impact factor: 3.307

6.  A novel framework for horizontal and vertical data integration in cancer studies with application to survival time prediction models.

Authors:  Iliyan Mihaylov; Maciej Kańduła; Milko Krachunov; Dimitar Vassilev
Journal:  Biol Direct       Date:  2019-11-21       Impact factor: 4.540

  6 in total

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