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Enhancing Bioactive Compound Classification through the Synergy of Fourier-Transform Infrared Spectroscopy and Advanced Machine Learning Methods
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Orientador(es)
Resumo(s)
Bacterial infections and resistance to antibiotic drugs represent the highest challenges to public health. The search for new and promising compounds with anti-bacterial activity is a very urgent matter. To promote the development of platforms enabling the discovery of compounds with anti-bacterial activity, Fourier-Transform Mid-Infrared (FT-MIR) spectroscopy coupled with machine learning algorithms was used to predict the impact of compounds extracted from Cynara cardunculus against Escherichia coli. According to the plant tissues (seeds, dry and fresh leaves, and flowers) and the solvents used (ethanol, methanol, acetone, ethyl acetate, and water), compounds with different compositions concerning the phenol content and antioxidant and antimicrobial activities were obtained. A principal component analysis of the spectra allowed us to discriminate compounds that inhibited E. coli growth according to the conventional assay. The supervised classification models enabled the prediction of the compounds’ impact on E. coli growth, showing the following values for accuracy: 94% for partial least squares-discriminant analysis; 89% for support vector machine; 72% for k-nearest neighbors; and 100% for a backpropagation network. According to the results, the integration of FT-MIR spectroscopy with machine learning presents a high potential to promote the discovery of new compounds with antibacterial activity, thereby streamlining the drug exploratory process.
Descrição
Funding Information: Funding was provided by ILIND\u2013Lus\u00F3fona University through the PLABIA\u2013Research Platform for Bioactive Compounds through Artificial Intelligence. Publisher Copyright: © 2024 by the authors.
Palavras-chave
antimicrobial BPN Cynara cardunculus KNN machine learning MIR-Spectroscopy PCA PLS-DA SVM Microbiology Biochemistry Pharmacology, Toxicology and Pharmaceutics(all) Microbiology (medical) Infectious Diseases Pharmacology (medical) SDG 3 - Good Health and Well-being