近年来多巴胺D3受体(DRD_(3))在神经系统疾病的治疗过程中受到大量关注,包括帕金森、精神分裂、药物依赖等。本文综述2015年至今多巴胺D3受体选择性配体的研究进展,并以分子动力学原理为基础,利用Discovery Studio 4.5软件评价了这些配...近年来多巴胺D3受体(DRD_(3))在神经系统疾病的治疗过程中受到大量关注,包括帕金森、精神分裂、药物依赖等。本文综述2015年至今多巴胺D3受体选择性配体的研究进展,并以分子动力学原理为基础,利用Discovery Studio 4.5软件评价了这些配体的选择性,建立了基于分子共同特征的药效团模型,从类药分子库中筛选出对设计新型配体具有先导化合物意义的小分子化合物,以期对D3受体选择性配体的研究提供参考。展开更多
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 m...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<sub>LUMO+1</sub> (one level above lowest unoccupied molecular orbital energy), d(C<sub>6</sub>-C<sub>5</sub>) (distance between C<sub>6</sub> and C<sub>5</sub> 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.展开更多
文摘近年来多巴胺D3受体(DRD_(3))在神经系统疾病的治疗过程中受到大量关注,包括帕金森、精神分裂、药物依赖等。本文综述2015年至今多巴胺D3受体选择性配体的研究进展,并以分子动力学原理为基础,利用Discovery Studio 4.5软件评价了这些配体的选择性,建立了基于分子共同特征的药效团模型,从类药分子库中筛选出对设计新型配体具有先导化合物意义的小分子化合物,以期对D3受体选择性配体的研究提供参考。
文摘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<sub>LUMO+1</sub> (one level above lowest unoccupied molecular orbital energy), d(C<sub>6</sub>-C<sub>5</sub>) (distance between C<sub>6</sub> and C<sub>5</sub> 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.
