Computational Tools and Resources for Metabolism-Related Property Predictions. 1. Overview of Publicly Available (Free and Commercial) Databases and Software

Abstract

Metabolism has been identified as a defining factor in drug development success or failure because of its impact on many aspects of drug pharmacology, including bioavailability, half-life and toxicity. In this article, we provide an outline and descriptions of the resources for metabolism-related property predictions that are currently either freely or commercially available to the public. These resources include databases with data on, and software for prediction of, several end points: metabolite formation, sites of metabolic transformation, binding to metabolizing enzymes and metabolic stability. We attempt to place each tool in historical context and describe, wherever possible, the data it was based on. For predictions of interactions with metabolizing enzymes, we show a typical set of results for a small test set of compounds. Our aim is to give a clear overview of the areas and aspects of metabolism prediction in which the currently available resources are useful and accurate, and the areas in which they are inadequate or missing entirely.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2012
Accession Number
ADA579472

Entities

People

  • Alexey V. Zakharov
  • Anders Wallqvist
  • Angelo Pugliese
  • Gregory Tawa
  • Marc C. Nicklaus
  • Megan L. Peach
  • Ruifeng Liu

Organizations

  • United States Army Medical Research and Development Command

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Application Software
  • Chemical Compounds
  • Chemical Synthesis
  • Chemistry
  • Computational Biology
  • Computational Science
  • Computer Programming
  • Computer Programs
  • Computers
  • Graphical User Interface
  • Information Systems
  • Machine Learning
  • Metabolism
  • Microsomes
  • Pharmacology
  • Small Molecules
  • Supervised Machine Learning

Readers

  • Computational Modeling and Simulation
  • Software Engineering.
  • Toxicology/Environmental Toxicology