A new approach to atom-to-atom mapping using the naive Bayesian classifier
The key step in the computer analysis of chemical reactions is the determination of the correspondence between the atoms of reagents and products. This procedure is called atom-to-atom mapping (AAM). The presence of AAM is a key factor for establishing the mechanism and type of reaction, searching f...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Kazan Federal University
2018-06-01
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| Series: | Učënye Zapiski Kazanskogo Universiteta. Seriâ Estestvennye Nauki |
| Subjects: | |
| Online Access: | https://kpfu.ru/a-new-approach-to-atom-to-atom-mapping-using-the_342806.html |
| Summary: | The key step in the computer analysis of chemical reactions is the determination of the correspondence between the atoms of reagents and products. This procedure is called atom-to-atom mapping (AAM). The presence of AAM is a key factor for establishing the mechanism and type of reaction, searching for similarities and substructures, modeling, checking the quality of data. A new approach has been proposed to the search for optimal atomic-atom mapping in chemical reactions based on the use of machine learning methods. The learning task is formulated as a classification: for each pair of the reagent-product atom, it is necessary to establish their assignment to the correct/incorrect mapping. We have used a simple naive Bayesian classifier. The approach described in this paper is the first example of a self-learning algorithm for AAM. |
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| ISSN: | 2542-064X 2500-218X |