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Toward generating simpler QSAR models: nonlinear multivariate regression versus several neural network ensembles and
Bono Lucić1, Damir Nadramija, Ivan Basic
1The Rugjer Bosković Institute, P.O. Box 180, HR-10002 Zagreb, Croatia. lucic@irb.hr
Journal of Chemical Information and Computer Sciences
|July 23, 2003
Summary
Simple multiregression (MR) models, generated using CROMRsel and Genetic Function Approximation (GFA), demonstrated superior accuracy and simplicity compared to Neural Network Ensemble (NNE) models in QSAR/QSPR studies. CROMRsel outperformed GFA in selecting optimal descriptors for these predictive models.
Area of Science:
- Computational chemistry and cheminformatics
- Quantitative Structure-Activity/Property Relationships (QSAR/QSPR)
- Machine learning in drug discovery and chemical research
Background:
- Neural Network Ensembles (NNEs) are robust but complex modeling techniques.
- The efficacy of simpler modeling procedures, like multiregression (MR), in achieving comparable accuracy to NNEs is under investigation.
- Previous QSAR studies have utilized NNEs on various chemical datasets.
Purpose of the Study:
- To evaluate if simple multiregression (MR) models can match the accuracy of complex Neural Network Ensemble (NNE) models.
- To compare the performance of two MR model generation/selection procedures: CROMRsel and Genetic Function Approximation (GFA).
- To assess the simplicity and interpretability of MR models versus NNEs.
Main Methods:
- Application of CROMRsel and GFA methods to generate linear and nonlinear MR models.
- Comparison of MR models with NNE models using four established QSAR datasets (benzodiazepines, carboquinones, pyrimidines, antimycin analogues).
- Introduction of nonlinearity in MR models via cross-products of descriptors and selection of top-performing descriptors.
Main Results:
- MR models consistently exhibited better cross-validated statistical parameters than NNE models across all datasets.
- The CROMRsel method yielded slightly better MR models compared to the GFA method.
- MR models were significantly simpler and expressed relationships in a clear functional form, outperforming other tested modeling approaches.
Conclusions:
- Simple MR models, particularly those selected by CROMRsel, are highly accurate and preferable to NNEs for the studied QSAR problems.
- NNEs may not be optimal for small datasets, suggesting their application is better suited for larger data scenarios.
- The study confirms the utility of simpler, interpretable models in QSAR/QSPR research.