Ngo Hanna Joelleinsert, Nziko Vincent de Paul N, Ntie-Kang Fidele, Mbah James A, Toze Flavien A A
Department of Chemistry, Faculty of Science, University of Buea, P. O. Box 63, Buea, Cameroon.
Department of Chemistry, Faculty of Science, University of Douala, P. O. Box 24157, Douala, Cameroon.
Heliyon. 2021 May 21;7(5):e07032. doi: 10.1016/j.heliyon.2021.e07032. eCollection 2021 May.
A quantitative structure-activity relationship (QSAR) study was conducted using nineteen previously synthesized, and tested 1-aryl-6-hydroxy-1,2,3,4-tetrahydroisoquinolines with proven activities against . In order to computationally design and screen potent antimalarial agents, these compounds with known biological activity ranging from 0.697 to 35.978 μM were geometry optimized at the B3LYP/6-311 + G(d,p) level of theory, using the Gaussian 09W software. To calculate the topological differences, the series of the nineteen compounds was superimposed and a hypermolecule obtained with = 17 and 20 vertices. Other molecular descriptors were considered in order to build a highly predictive QSAR model. These include the minimal topological differences (MTD), LogP, two dimensional polarity surface area (TDPSA), dipole moment (μ), chemical hardness (η), electrophilicity (ω), potential energy (E), electrostatic energy (E) and number of rotatable bonds (NRB). By using a training set composed of 15 randomly selected compounds from this series, several QSAR equations were derived. The QSAR equations obtained were then used to attempt to predict the IC values of 4 remaining compounds in a test (or validation) set. Ten analogues were proposed by a fragment search of a fragment library containing the pharmacophore model of the active compounds contained in the training set. The most active proposed analogue showed a predicted activity within the lower micromolar range.
利用19种先前合成并测试过的1-芳基-6-羟基-1,2,3,4-四氢异喹啉进行了定量构效关系(QSAR)研究,这些化合物对……具有已证实的活性。为了通过计算设计和筛选有效的抗疟药物,使用高斯09W软件,在B3LYP/6-311 + G(d,p)理论水平上对这些已知生物活性范围为0.697至35.978 μM的化合物进行几何优化。为了计算拓扑差异,将这19种化合物系列进行叠加,得到一个具有17和20个顶点的超分子。还考虑了其他分子描述符,以建立一个高度预测性的QSAR模型。这些描述符包括最小拓扑差异(MTD)、LogP、二维极性表面积(TDPSA)、偶极矩(μ)、化学硬度(η)、亲电性(ω)、势能(E)、静电能(E)和可旋转键的数量(NRB)。通过使用从该系列中随机选择的15种化合物组成的训练集,推导出了几个QSAR方程。然后使用得到的QSAR方程来尝试预测测试(或验证)集中其余4种化合物的IC值。通过对包含训练集中活性化合物药效团模型的片段库进行片段搜索,提出了10种类似物。所提出的最具活性的类似物显示出预测活性在低微摩尔范围内。