A Novel Method for Building Regression Tree Models for QSAR Based on Artificial Ant Colony Systems
Abstract
Among the multitude of learning algorithms that can be employed for deriving quantitative structure-activity relationships, regression trees have the advantage of being able to handle large data sets, dynamically perform the key feature selection, and yield readily interpretable models. A conventional method of building a regression tree model is recursive partitioning, a fast greedy algorithm that works well in many, but not all, cases. This work introduces a novel method of data partitioning based on artificial ants. This method is shown to perform better than recursive partitioning on three well-studied data sets.
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A feature selection algorithm based on artificial ant colonies was used to identify the subset of robotic metrics with the highest predictive power in order to minimize over-fitting.
“To minimize over-fitting, a feature selection algorithm based on artificial ant colonies, originally developed for QSAR applications, was used to identify the subset of robotic metrics that had the highest predictive power.280”
From QSAR without borders (2020) · cites this paper as [280]
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Ant colony system: a cooperative learning approach to the traveling salesman problem
Dorigo, M., & Gambardella, L. M. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585892 · Cited by 8,081 · see citing works
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Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913 · see citing works
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- Authors
- Izrailev, S., & Agrafiotis, D.
- Affiliations
- 3-Dimensional Pharmaceuticals, Inc., 665 Stockton Drive, Exton, Pennsylvania 19341
- ORCID
- Agrafiotis, Dimitris
- Venue
- Journal of Chemical Information and Computer Sciences
- Publisher
- American Chemical Society (ACS)
- Published
- 2000-11-01
- Volume
- 41
- Issue
- 1
- Pages
- 176-180
- ISSN
- 0095-2338 (ISSN-L)
- 1520-5142
- OpenAlex
- W2091172912
- Cited by
- 66
- References
- 1
- Citations by year
- 2022: 1 · 2021: 2 · 2020: 2 · 2019: 1 · 2018: 2 · 2017: 1 · 2016: 1 · 2015: 4 · 2014: 2 · 2013: 1 · 2012: 2
- FWCI
- 6.72
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Chemical Information and Computer Sciences (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- regression trees, QSAR, data partitioning, feature selection, recursive partitioning, ant colony optimization
- MeSH terms
- Algorithms, Animals, Ants, Models, Chemical, Quantitative Structure-Activity Relationship
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