Exploring Antimalarial Activity of Drugs using Weighted Atomic Vectors and Artificial Intelligence.

Journal: Journal of vector borne diseases
Published Date: Jun 10, 2025

Abstract

BACKGROUND OBJECTIVES: Malaria is a global health issue, causing over two million deaths annually. The development of new and potent antimalarial drugs is essential to combat the disease. Machine learning has been increasingly applied to predict antimalarial activity of compounds, offering a promising approach for antimalarial pharmaceutical research. This study aims to predict the antimalarial activity of potential compounds using weighted atomic vectors and machine learning algorithms.

Authors

  • Yoan Martínez López
    Department of Computer Sciences, Faculty of Informatics, Camagüey University, Camagüey City, 74650, Cuba.
  • Wilber Figueredo Rodríguez
    Department of Computer Sciences, Faculty of Informatics, Camagüey University, Camagüey City, 74650, Cuba.
  • Juan A Castillo-Garit
    Instituto Universitario de Investigación y Desarrollo Tecnológico (IDT), Universidad Tecnológica Metropolitana, Ignacio Valdivieso 2409, San Joaquín, Santiago, Chile.
  • Stephen J Barigye
    Departamento de Química Física Aplicada, Facultad de Ciencias, Universidad Autónoma de Madrid (UAM), 28049, Madrid, Spain. sjbarigye@gmail.com.
  • Oscar Martínez-Santiago
    Alfa Vitamins Laboratories, Miami, FL, 33166, USA.
  • Noel Enrique Rodríguez Maya
    Instituto Tecnológico de Zitácuaro, División de Estudios de Posgrado e Investigación, Zitácuaro, Michoacán, México.

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