Neural networks in ADME and toxicity prediction

Research output: Contribution to journalArticleOther

19 Citations (Scopus)

Abstract

The inability to understand and control the ADMET properties of molecules is an important reason why many candidate drugs fail late in the development pathway. Unfavorable pharmacokinetics, metabolism or toxicity, for example, can cause development candidates to be dropped. These failures are expensive and they contribute to the diminishing efficiency of the pharmaceutical industry. In silico models of ADMET properties allow these properties to be considered at an early, less costly stage, and should reduce the number of late-stage development candidates which fail. ADMET properties are multifactorial and complex, requiring very flexible methods to build predictive in silico models. This review summarizes the contribution neural networks are making to the development of useful ADMET models.

Original languageEnglish
Pages (from-to)1043-1057
Number of pages15
JournalDrugs of the Future
Volume29
Issue number10
DOIs
Publication statusPublished - Oct 2004
Externally publishedYes

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