Automatic NMR-Based Identification of Chemical Reaction Types in Mixtures of Co-Occurring Reactions

• Published in: PloS one Vol. 9; no. 2; p. e88499
• Main Authors: Latino, Diogo A. R. S., Aires-de-Sousa, João
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 13.02.2014; Public Library of Science (PLoS)
• Subjects: Algorithms; Artificial Intelligence; Automatic control; Azirines - chemistry; Chemical reactions; Chemistry; Classification; Clustering; Coding; Computer Science; Cycloaddition Reaction; Cycloparaffins - chemistry; Data processing; Identification methods; Information management; Learning algorithms; Machine learning; Magnetic Resonance Spectroscopy - statistics & numerical data; Metabolism; Metabolites; Molecular structure; Neural networks; NMR; Nuclear magnetic resonance; Photochemical Processes; Pyridazines - chemistry; Quality control; Reproducibility of Results; Self organizing maps; Teaching methods; Test sets
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0088499

The combination of chemoinformatics approaches with NMR techniques and the increasing availability of data allow the resolution of problems far beyond the original application of NMR in structure elucidation/verification. The diversity of applications can range from process monitoring, metabolic profiling, authentication of products, to quality control. An application related to the automatic analysis of complex mixtures concerns mixtures of chemical reactions. We encoded mixtures of chemical reactions with the difference between the (1)H NMR spectra of the products and the reactants. All the signals arising from all the reactants of the co-occurring reactions were taken together (a simulated spectrum of the mixture of reactants) and the same was done for products. The difference spectrum is taken as the representation of the mixture of chemical reactions. A data set of 181 chemical reactions was used, each reaction manually assigned to one of 6 types. From this dataset, we simulated mixtures where two reactions of different types would occur simultaneously. Automatic learning methods were trained to classify the reactions occurring in a mixture from the (1)H NMR-based descriptor of the mixture. Unsupervised learning methods (self-organizing maps) produced a reasonable clustering of the mixtures by reaction type, and allowed the correct classification of 80% and 63% of the mixtures in two independent test sets of different similarity to the training set. With random forests (RF), the percentage of correct classifications was increased to 99% and 80% for the same test sets. The RF probability associated to the predictions yielded a robust indication of their reliability. This study demonstrates the possibility of applying machine learning methods to automatically identify types of co-occurring chemical reactions from NMR data. Using no explicit structural information about the reactions participants, reaction elucidation is performed without structure elucidation of the molecules in the mixtures.