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Adv Drug Deliv Rev. 2006 Nov 30;58(12-13):1409-30. Epub 2006 Sep 22.

Application of data mining approaches to drug delivery.

Author information

1
ACT LLC, 1 Penn Plaza-36th Floor, New York, NY 10119, USA. ekinssean@yahoo.com

Abstract

Computational approaches play a key role in all areas of the pharmaceutical industry from data mining, experimental and clinical data capture to pharmacoeconomics and adverse events monitoring. They will likely continue to be indispensable assets along with a growing library of software applications. This is primarily due to the increasingly massive amount of biology, chemistry and clinical data, which is now entering the public domain mainly as a result of NIH and commercially funded projects. We are therefore in need of new methods for mining this mountain of data in order to enable new hypothesis generation. The computational approaches include, but are not limited to, database compilation, quantitative structure activity relationships (QSAR), pharmacophores, network visualization models, decision trees, machine learning algorithms and multidimensional data visualization software that could be used to improve drug delivery after mining public and/or proprietary data. We will discuss some areas of unmet needs in the area of data mining for drug delivery that can be addressed with new software tools or databases of relevance to future pharmaceutical projects.

PMID:
17081647
DOI:
10.1016/j.addr.2006.09.005
[Indexed for MEDLINE]

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