Abstract |
Aminopeptidase N (APN) inhibitors have been reported to be effective in treating of life threatening diseases including cancer. Validated ligand- and structure-based pharmacophore mapping approaches were combined with Bayesian modeling and recursive partitioning to identify structural and physicochemical requirements for highly active APN inhibitors. Based on the assumption that ligand- and structure-based pharmacophore models are complementary, the efficacy of 'multiple pharmacophore screening' for filtering true positive virtual hits was investigated. These multiple pharmacophore screening methods were utilized to search novel virtual hits for APN inhibition. The number of hits was refined and reduced by recursive partitioning, drug-likeliness, pharmacokinetic property prediction, and comparative molecular-docking studies. Four compounds were proposed as the potential virtual hits for APN enzyme inhibition.
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Authors | Amit K Halder, Achintya Saha, Tarun Jha |
Journal | Molecular diversity
(Mol Divers)
Vol. 17
Issue 1
Pg. 123-37
(Feb 2013)
ISSN: 1573-501X [Electronic] Netherlands |
PMID | 23341006
(Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
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Chemical References |
- Anilides
- Ligands
- Protease Inhibitors
- 1-leucine-4-nitroanilide
- CD13 Antigens
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Topics |
- Anilides
(metabolism)
- Animals
- Bayes Theorem
- CD13 Antigens
(antagonists & inhibitors)
- Computer Simulation
- Drug Design
- Drug Evaluation, Preclinical
(methods)
- Ligands
- Models, Molecular
- Molecular Docking Simulation
- Molecular Structure
- Protease Inhibitors
(chemistry, pharmacology)
- Quantitative Structure-Activity Relationship
- Swine
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