Supervised Self-Organizing Maps in Drug Discovery. 2. Improvements in Descriptor Selection and Model Validation
| Autor: | Ersin Bayram, Yun-De Xiao, Peter Santago, Rebecca Harris, Jeffrey Daniel Schmitt |
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| Rok vydání: | 2005 |
| Předmět: |
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 |
| Zdroj: | Journal of Chemical Information and Modeling. 46:137-144 |
| ISSN: | 1549-960X 1549-9596 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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