Paper Info
Title
Drugs and Drug-Like Compounds: Discriminating Approved Pharmaceuticals from Screening-Library Compounds
Abstract
Compounds in drug screening-libraries should resemble pharmaceuticals. To operationally test this, we analysed the compounds in terms of known drug-like filters and developed a novel machine learning method to discriminate approved pharmaceuticals from "drug-like" compounds. This method uses both structural features and molecular properties for discrimination. The method has an estimated accuracy of 91% in discriminating between the Maybridge HitFinder library and approved pharmaceuticals, and 99% between the NATDiverse collection (from Analyticon Discovery) and approved pharmaceuticals. These results show that Lipinski's Rule of 5 for oral absorption is not sufficient to describe "drug-likeness" and be the main basis of screening-library design.
Year
DOI
Venue
2009
10.1007/978-3-642-04031-3_29
PRIB
Keywords
Field
DocType
analyticon discovery,novel machine,screening-library compounds,drug-like filter,natdiverse collection,maybridge hitfinder library,molecular property,discriminate approved pharmaceuticals,drug screening-libraries,approved pharmaceuticals,drug-like compounds,main basis,estimated accuracy,rule of 5,machine learning
Inductive logic programming,Computer science,Artificial intelligence,Lipinski's rule of five,Drug,Machine learning
Conference
Volume
ISSN
Citations 
5780
0302-9743
0
PageRank 
References 
Authors
0.34
11
2
Name
Order
Citations
PageRank
Amanda C. Schierz1292.66
Ross D. King21774194.85