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  

作　　者：Jeferson Stiver Oliveira de Castro José Ciríaco Pinheiro Sílvia Simone dos Santos de Morais Heriberto Rodrigues Bitencourt Antonio Florêncio de Figueiredo Marcos Antonio Barros dos Santos Fábio dos Santos Gil Ana Cecília Barbosa Pinheiro Jeferson Stiver Oliveira de Castro;José Ciríaco Pinheiro;Sílvia Simone dos Santos de Morais;Heriberto Rodrigues Bitencourt;Antonio Florêncio de Figueiredo;Marcos Antonio Barros dos Santos;Fábio dos Santos Gil;Ana Cecília Barbosa Pinheiro(Instituto de Educação, Ciência e Tecnologia do Pará, Castanhal, PA, Brasil;Laboratório de Química Teórica e Computacional, Universidade Federal do Pará, Belém, PA, Brasil;Instituto Amazônia dos Saberes, São Luís, MA, Brasil;Universidade do Estado do Amapá, Macapá, AP, Brasil;Laboratório de Síntese, Universidade Federal do Pará, Belém, PA, Brasil;Universidade do Estado do Pará, Belém, PA, Brasil;Fundação Santa Casa de Misericórdia do Pará, Belém, PA, Brasil)

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

出　　处：《Journal of Biophysical Chemistry》2023年第1期1-29,共29页生物物理化学（英文）

摘　　要：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.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.

关 键 词：Antimalarial Design MEP Ligand-Receptor Interaction Supervised Machine Learning Methods Models Built with Supervised Machine Learning Methods 

分 类 号：O62[理学—有机化学]