Artificial neural networks in drug design II: Influence of learning rate and momentum factor on the predictive ability
- Author(s)
- Dominik Kaiser, Claudia Tmej, Peter Chiba, Klaus-Jürgen Schaper, Gerhard Ecker
- Abstract
A data set of 48 propafenone-type modulators of multidrug resistance was used to investigate the influence of learning rate and momentum factor on the predictive power of artificial neural networks of different architecture. Generally, small learning rates and medium sized momentum factors are preferred. Some of the networks showed higher cross validated Q2 values than the corresponding linear model (0.87 vs. 0.83). Screening of a 158 compound virtual library identified several new lead compounds with activities in the nanomolar range.
- Organisation(s)
- External organisation(s)
- Research Center Borstel - Leibniz Lung Center
- Journal
- Scientia Pharmaceutica
- Volume
- 68
- Pages
- 57-64
- No. of pages
- 8
- ISSN
- 0036-8709
- Publication date
- 2000
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 3012 Pharmacy, Pharmacology, Toxicology
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/b7fc7bc7-b139-428a-abc2-cb1066321428