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Machine learning approach to discovery of small molecules with potential inhibitory action against vasoactive metalloproteases

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Abstract

With the advancement of combinatorial chemistry and big data, drug repositioning has boomed. In this sense, machine learning and artificial intelligence techniques offer a priori information to identify the most promising candidates. In this study, we combine QSAR and docking methodologies to identify compounds with potential inhibitory activity of vasoactive metalloproteases for the treatment of cardiovascular diseases. To develop this study, we used a database of 191 thermolysin inhibitor compounds, which is the largest as far as we know. First, we use Dragon's molecular descriptors (0-3D) to develop classification models using Bayesian networks (Naive Bayes) and artificial neural networks (Multilayer Perceptron). The obtained models are used for virtual screening of small molecules in the international DrugBank database. Second, docking experiments are carried out for all three enzymes using the Autodock Vina program, to identify possible interactions with the active site of human metalloproteases. As a result, high-performance artificial intelligence QSAR models are obtained for training and prediction sets. These allowed the identification of 18 compounds with potential inhibitory activity and an adequate oral bioavailability profile, which were evaluated using docking. Four of them showed high binding energies for the three enzymes, and we propose them as potential dual ACE/NEP inhibitors for the control of blood pressure. In summary, the in silico strategies used here constitute an important tool for the early identification of new antihypertensive drug candidates, with substantial savings in time and money.

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Acknowledgements

Y. Cañizares-Carmenate and J. A. Castillo-Garit acknowledges to CELFI for the scholarships to participate in the course: Computational Biology Oriented to the Drugs Design (Universidad de Buenos Aires Nov/2016). F. Torrens acknowledges an internal aid from the Universidad Católica de Valencia San Vicente Mártir for support; K. Mena acknowledges to Fondecyt N° 11180650

Author information

Authors and Affiliations

  1. Unit of Computer-Aided Molecular ‘‘Biosilico” Discovery and Bioinformatic Research (CAMD-BIR Unit), Facultad de Química-Farmacia, Universidad Central ‘‘Marta Abreu” de Las Villas, 54830, Santa Clara, Villa Clara, Cuba

    Yudith Cañizares-Carmenate

  2. Departamento de Ciencias Biológicas Y Químicas, Facultad de Recursos Naturales, Universidad Católica de Temuco, Ave. Rudecindo Ortega, 02950, Temuco, Chile

    Karel Mena-Ulecia

  3. Núcleo de Investigación en Bioproductos Y Materiales Avanzados (BIOMA), Facultad de Ingeniería, Universidad Católica de Temuco, Ave. Rudecindo Ortega, 02950, Temuco, Chile

    Karel Mena-Ulecia

  4. Facultad de Ingeniería, Inorganic Chemistry and Molecular Materials Center, Instituto de Ciencias Químicas Aplicadas, Universidad Autónoma de Chile, El Llano Subercaseaux, San Miguel, 2801, Santiago, Chile

    Desmond MacLeod Carey

  5. Laboratorio de Bioinformática Y Química Computacional, Escuela de Química Y Farmacia, Facultad de Medicina, Universidad Católica de Maule, Talca, Chile

    Yunier Perera-Sardiña & Erix W. Hernández-Rodríguez

  6. Grupo de Medicina Molecular Y Traslacional (MeM & T), Escuela de Medicina, Universidad San Francisco de Quito, Edificio de Especialidades Médicas, Av. Interoceánica Km 12½, Quito, Ecuador

    Yovani Marrero-Ponce

  7. Institut Universitari de Ciència Molecular, Universitat de València, Edifici D’Instituts de Paterna, P.O. Box 22085, 46071, València, Spain

    Francisco Torrens

  8. Unidad de Toxicología Experimental, Universidad de Ciencias Médicas de Villa Clara, Carretera a Acueducto Y Circunvalación, CP: 50200, Santa Clara, Villa Clara, Cuba

    Juan A. Castillo-Garit

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  1. Yudith Cañizares-Carmenate
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  2. Karel Mena-Ulecia
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  8. Juan A. Castillo-Garit
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Correspondence to Juan A. Castillo-Garit.

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Cañizares-Carmenate, Y., Mena-Ulecia, K., MacLeod Carey, D. et al. Machine learning approach to discovery of small molecules with potential inhibitory action against vasoactive metalloproteases. Mol Divers 26, 1383–1397 (2022). https://doi.org/10.1007/s11030-021-10260-0

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