Group of Computational and Medicinal Chemistry, LMCE Laboratory, University of Biskra, Biskra, Algeria.
Product and Process Innovation Department, Qarshi Brands Pvt. Ltd. Hattar Industrial Estate, Haripur, KPK, Pakistan.
J Biomol Struct Dyn. 2024 Jul;42(10):5308-5320. doi: 10.1080/07391102.2023.2225007. Epub 2023 Jun 19.
The in silico evaluation of 27 p-aminosalicylic acid derivatives, also referred to as neuraminidase inhibitors was the focus of the current study. To search and predict new potential neuraminidase inhibitors, this study was based on the ligand-based pharmacophore modeling, 3D QSAR, molecular docking, ADMET and MD simulation studies. The data was generated from recently reported inhibitors and divided into two groups, one of these group has 17 compounds for training and the second group has 10 compounds for testing purpose. The generated pharmacophore has known as ADDPR_4 was found statistically significant 3D-QSAR model owing the high trust scores (R = 0.974, Q = 0.905, RMSE = 0.23). Morever external validation was also employed to evaluate the prediction capacity of the built pharmacophore model (R = 0.905). In addition, in silico ADMET, analyses were employed to evaluate the obtained hits for drug likeness properties. The stability of formed complexes was further evaluated using molecular dynamics. Top two hits showed stable complexes with Neuraminidase based on calculated total binding energy by MM-PBSA.Communicated by Ramaswamy H. Sarma.
当前研究的重点是对 27 种对氨基水杨酸衍生物（也称为神经氨酸酶抑制剂）进行计算机评估。为了寻找和预测新的潜在神经氨酸酶抑制剂，本研究基于基于配体的药效团建模、3D-QSAR、分子对接、ADMET 和 MD 模拟研究。该数据来源于最近报道的抑制剂，并分为两组，其中一组有 17 种化合物用于训练，另一组有 10 种化合物用于测试。生成的药效团被称为 ADDPR_4，由于置信分数高（R = 0.974，Q = 0.905，RMSE = 0.23），被发现是统计学上显著的 3D-QSAR 模型。此外，还采用外部验证来评估所构建药效团模型的预测能力（R = 0.905）。此外，还进行了计算机 ADMET 分析，以评估获得的命中物的药物相似性特性。使用分子动力学进一步评估形成复合物的稳定性。基于 MM-PBSA 计算的总结合能，前两个命中物与神经氨酸酶显示出稳定的复合物。由 Ramaswamy H. Sarma 传达。
