A REPRESENTATION TO APPLY USUAL DATA MINING TECHNIQUES TO CHEMICAL REACTIONS — ILLUSTRATION ON THE RATE CONSTANT OF S N 2 REACTIONS IN WATER
Abstract
Chemical reactions always involve several molecules of two types, reactants and products. Existing data mining techniques, eg. Quantitative Structure Activity Relationship (QSAR) methods, deal with individual molecules only. In this article, we propose to use a Condensed Graph of Reaction (CGR) to merge all molecules involved in a reaction into one molecular graph. This allows one to consider reactions as pseudo-molecules and to develop QSAR models based on fragment descriptors. Then ISIDA (In SIlico Design and Analysis) fragment descriptors built from CGRs are used to generate models for the rate constant of S N 2 reactions in water, using three usual attribute-value regression algorithms (linear regression, support vector machine, and regression trees). This approach is compared favorably to two state-of-the-art relational data mining techniques.
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- Authors
- HOONAKKER, F., LACHICHE, N., VARNEK, A., & WAGNER, A.
- Affiliations
- Université de Strasbourg (FR)
- Venue
- International Journal on Artificial Intelligence Tools
- Publisher
- World Scientific Pub Co Pte Ltd
- Published
- 2011-04
- Volume
- 20
- Issue
- 02
- Pages
- 253-270
- ISSN
- 0218-2130 (ISSN-L)
- 1793-6349
- OpenAlex
- W2011018037
- Cited by
- 65
- References
- 15
- Citations by year
- 2026: 8 · 2025: 10 · 2024: 6 · 2023: 8 · 2022: 2 · 2021: 13 · 2020: 3 · 2018: 6 · 2017: 3 · 2016: 2 · 2015: 1 · 2014: 1 · 2012: 2
- FWCI
- 0.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Artificial Intelligence Tools (publishedVersion)
- HAL (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Mathematics › Geometry and Topology › Graph theory and applications
- Keywords
- rate constants, SN2 reaction, aqueous-phase reactions, QSAR, relational data mining, support vector machine, regression trees, linear regression
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