Machine learning techniques for in silico modeling of drug metabolism.

Abstract:

:The computational assessment of drug metabolism has gained considerable interest in pharmaceutical research. Amongst others, machine learning techniques have been employed to model relationships between the chemical structure of a compound and its metabolic fate. Examples for these techniques, which were originally developed in fields far from drug discovery, are artificial neural networks or support vector machines. This paper summarizes the application of various machine learning techniques to predict the interaction between organic molecules and metabolic enzymes. More complex endpoints such as metabolic stability or in vivo clearance will also be addressed. It is shown that the prediction of metabolic endpoints with machine learning techniques has made considerable progress over the past few years. Depending on the procedure used, either classification or quantitative prediction is possible for even large and diverse compound sets. Together with the expanding experimental data basis, these in silico methods have become valuable tools in the drug discovery and development process.

journal_name

Curr Top Med Chem

authors

Fox T,Kriegl JM

doi

10.2174/156802606778108915

subject

Has Abstract

pub_date

2006-01-01 00:00:00

pages

1579-91

issue

15

eissn

1568-0266

issn

1873-4294

journal_volume

6

pub_type

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