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Supervised Self-Organizing Maps in Drug Discovery. 2. Improvements in Descriptor Selection and Model Validation
- Source :
- Journal of Chemical Information and Modeling. 46:137-144
- Publication Year :
- 2005
- Publisher :
- American Chemical Society (ACS), 2005.
-
Abstract
- The modeling of nonlinear descriptor-target relationships is a topic of considerable interest in drug discovery. We, herein, continue reporting the use of the self-organizing map-a nonlinear, topology-preserving pattern recognition technique that exhibits considerable promise in modeling and decoding these relationships. Since simulated annealing is an efficient tool for solving optimization problems, we combined the supervised self-organizing map with simulated annealing to build high-quality, highly predictive quantitative structure-activity/property relationship models. This technique was applied to six data sets representing a variety of biological endpoints. Since a high statistical correlation in the training set does not indicate a highly predictive model, the quality of all the models was confirmed by withholding a portion of each data set for external validation. Finally, we introduce new cross-validation and dynamic partitioning techniques to address model overfitting and assessment.
- Subjects :
- Self-organizing map
Optimization problem
Computer science
Property (programming)
General Chemical Engineering
Drug Evaluation, Preclinical
Quantitative Structure-Activity Relationship
Library and Information Sciences
Overfitting
Machine learning
computer.software_genre
Models, Biological
Artificial neural network
business.industry
Reproducibility of Results
General Chemistry
Computer Science Applications
Data set
Pattern recognition (psychology)
Simulated annealing
Neural Networks, Computer
Data mining
Artificial intelligence
business
computer
Algorithms
Subjects
Details
- ISSN :
- 1549960X and 15499596
- Volume :
- 46
- Database :
- OpenAIRE
- Journal :
- Journal of Chemical Information and Modeling
- Accession number :
- edsair.doi.dedup.....4ecb3dad2459806cd4d416c88b394946