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A new approach to atom-to-atom mapping using the naive Bayesian classifier

A.I. Khayrullinaa*, T.I. Madzhidova**, R.I. Nugmanova***, V.A. Afoninaa****, I.I. Baskinb*****, A.A. Varneka,c******

aKazan Federal University, Kazan, 420008 Russia

bMoscow State University, Moscow, 119991, Russia

cUniversity of Strasbourg, Strasbourg, 67084 France

E-mail: *adelihajrullina@kpfu.ru, **Timur.Madzhidov@kpfu.ru, ***rainugmanov@kpfu.ru, ****ValAAfonina@kpfu.ru, *****igbaskin@gmail.com, ******varnek@unistra.fr

Received November 16, 2017

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Abstract

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.

Keywords: atom-to-atom mapping, chemical reactions, machine learning, classification, chemoinformatics

Acknowledgments. The study was supported by the Russian Science Foundation (project no. 14-43-00024).

Figure Captions

Fig. 1. Atom-to-atom mapping in reactions. Numbers indicate the correspondence of atoms. The figure shows the correct AAM, with the broken C8–I7 bond and the created C8–N4 bond.

Fig. 2. Generation of pairs and corresponding values of AAM.

Fig. 3. Generation of fragment des­criptors for CS6.

Fig. 4. Forming a bit string of hashed molecular fingerprints (X) based on fragment des­criptors: a) CS6; b) CP6.

Fig. 5. Operations on bit strings of hashed molecular fingerprints.

Fig. 6. Table with the values of the probabilities of atom-atom mapping (pij,%), where i – row corresponding to product atoms, j – column corresponding to reagent atoms. Selected elements correspond to the pairs with the correct AAM.

Fig. 7. Dependence of the accuracy of predictions on the fragmentation length. Model quality when minimal fragment length is equal to one atom is shown by solid line, the one when minimal fragment length is equal to two atoms is shown by dotted line.

Fig. 8. Dependence of the accuracy of predictions on the length of the bit string.

Fig. 9. Comparison of the accuracy of predictions using different ways of atomic fingerprint combinations to form atomic pair bitstring (A – reagent atom fingerprint, B – product atom fingerprint).

Fig. 10. Reactions with correct AAM (a) and erroneously predicted AAM (b).

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For citation: Khayrullina A.I., Madzhidov T.I., Nugmanov R.I., Afonina V.A., Baskin I.I., Varnek A.A. A new approach to atom-to-atom mapping using the naive Bayesian classifier. Uchenye Zapiski Kazanskogo Universiteta. Seriya Estestvennye Nauki, 2018, vol. 160, no. 2, pp. 200–213. (In Russian)


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A new approach to atom-to-atom mapping using the naive Bayesian classifier