Author(s)

Jeferson Stiver Oliveira de Castro¹, José Ciríaco Pinheiro^2,3*, Sílvia Simone dos Santos de Morais⁴, Heriberto Rodrigues Bitencourt⁵, Antonio Florêncio de Figueiredo¹, Marcos Antonio Barros dos Santos⁶, Fábio dos Santos Gil², Ana Cecília Barbosa Pinheiro⁷

¹Instituto de Educação, Ciência e Tecnologia do Pará, Castanhal, PA, Brasil.
²Laboratório de Química Teórica e Computacional, Universidade Federal do Pará, Belém, PA, Brasil.
³Instituto Amazônia dos Saberes, São Luís, MA, Brasil.
⁴Universidade do Estado do Amapá, Macapá, AP, Brasil.
⁵Laboratório de Síntese, Universidade Federal do Pará, Belém, PA, Brasil.
⁶Universidade do Estado do Pará, Belém, PA, Brasil.
⁷Fundação Santa Casa de Misericórdia do Pará, Belém, PA, Brasil.

N-11-azaartemisinins potentially active against Plasmodium falciparum are designed by combining molecular electrostatic potential (MEP), ligand-receptor interaction, and models built with supervised machine learning methods (PCA, HCA, KNN, SIMCA, and SDA). The optimization of molecular structures was performed using the B3LYP/6-31G* approach. MEP maps and ligand-receptor interactions were used to investigate key structural features required for biological activities and likely interactions between N-11-azaartemisinins and heme, respectively. The supervised machine learning methods allowed the separation of the investigated compounds into two classes: cha and cla, with the properties ε_LUMO+1 (one level above lowest unoccupied molecular orbital energy), d(C₆-C₅) (distance between C₆ and C₅ atoms in ligands), and TSA (total surface area) responsible for the classification. The insights extracted from the investigation developed and the chemical intuition enabled the design of sixteen new N-11-azaartemisinins (prediction set), moreover, models built with supervised machine learning methods were applied to this prediction set. The result of this application showed twelve new promising N-11-azaartemisinins for synthesis and biological evaluation.

Antimalarial Design, MEP, Ligand-Receptor Interaction, Supervised Machine Learning Methods, Models Built with Supervised Machine Learning Methods

Castro, J. , Pinheiro, J. , Morais, S. , Bitencourt, H. , Figueiredo, A. , Santos, M. , Gil, F. and Pinheiro, A. (2023) Design of N-11-Azaartemisinins Potentially Active against Plasmodium falciparum by Combined Molecular Electrostatic Potential, Ligand-Receptor Interaction and Models Built with Supervised Machine Learning Methods. Journal of Biophysical Chemistry, 14, 1-29. doi: 10.4236/jbpc.2023.141001.