2-芳基喹喔啉衍生物作为α-淀粉酶抑制剂的计算研究

IF 2.218 Q2 Chemistry

Chemical Data Collections Pub Date : 2023-10-01 DOI:10.1016/j.cdc.2023.101079

Lhoucine Naanaai , Abdellah El Aissouq , Hicham Zaitan , Mohammed Bouachrine , Fouad Khalil

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引用次数: 0

摘要

结合3D-QSAR建模、分子对接和药代动力学特性(ADMET)的计算分析，发现了对各种2-芳基喹诺啉衍生物具有有效抑制作用的新型配体。PLS和比较分子相似指数分析(CoMSIA)具有良好的相关性和预测能力(r2 = 0.904, q2 = 0.708, SEE = 0.064)，建立了最佳的3D-QSAR模型。空间场、静电场、疏水场和氢键受体对生物活性的变化有重要影响，主要有四个组成部分。许多新化合物被开发出来，并在这些可观的结果的基础上进行了计算机药物相似性、ADMET和分子对接研究。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Computational study of 2-aryl quinoxaline derivatives as α-amylase inhibitors

A computational analysis combining 3D-QSAR modeling, molecular docking, and pharmacokinetic properties (ADMET), led to the discovery of novel ligands with potent inhibitory effects on various 2-aryl quinoxaline derivatives. PLS and comparative molecular similarity index analysis (CoMSIA), which showed good correlative and predictive abilities (r² = 0.904, q² = 0.708, and SEE = 0.064), were used to create the best 3D-QSAR model. Steric, electrostatic, hydrophobic fields and hydrogen bond acceptors have a substantial impact on the change in biological activity with four main components. A number of new compounds were developed and subjected to in-silico drug similarity, ADMET and molecular docking studies based on these respectable results.

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来源期刊

Chemical Data Collections Chemistry-Chemistry (all)

CiteScore

6.10

自引率

0.00%

发文量

169

审稿时长

24 days

期刊介绍： Chemical Data Collections (CDC) provides a publication outlet for the increasing need to make research material and data easy to share and re-use. Publication of research data with CDC will allow scientists to: -Make their data easy to find and access -Benefit from the fast publication process -Contribute to proper data citation and attribution -Publish their intermediate and null/negative results -Receive recognition for the work that does not fit traditional article format. The research data will be published as ''data articles'' that support fast and easy submission and quick peer-review processes. Data articles introduced by CDC are short self-contained publications about research materials and data. They must provide the scientific context of the described work and contain the following elements: a title, list of authors (plus affiliations), abstract, keywords, graphical abstract, metadata table, main text and at least three references. The journal welcomes submissions focusing on (but not limited to) the following categories of research output: spectral data, syntheses, crystallographic data, computational simulations, molecular dynamics and models, physicochemical data, etc.