Conducting 2D and 3D QSAR Analyses and Molecular Docking Studies of Analogues of 2-(1-(1,3,4-thiadiazol-2-yl)piperidin-4-yl)ethan-1-ol with the Aim of Identifying Promising Drug Candidates for Targeting Glioblastoma

Meichen Pan, Lingxue Cheng, Yi-guo Wang, C. Lyu, Chao Hou, Qiming Zhang
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Abstract

2-(1-(1,3,4-thiadiazol-2-yl)piperidin-4-yl) ethan-1-ol analogues represent novel glutaminase 1 inhibitors. Their exemplary antineoplastic efficacy underscores their prospective utility in glioblastoma chemotherapy. This study aimed to elucidate 2D and 3D-QSAR models that authenticate the antineoplastic efficacy of ethan-1-ol analogues and delineate optimal structural configurations conducive to new pharmaceutical design. The Heuristic Method (HM) was employed for the development of a 2D-linear QSAR paradigm, whilst the Gene Expression Programming (GEP) algorithm was employed for a 2D-nonlinear QSAR paradigm. Concurrently, the CoMSIA methodology was deployed to scrutinize the nexus between pharmaceutical structure and potency. An ensemble of 200 nascent anti-glioma ethan-1-ol compounds was conceptualized, and their potency levels were prognosticated via chemical descriptors and molecular field delineations. Pharmaceuticals epitomizing peak potency were earmarked for molecular docking validation. The empirical modeling exhibited pronounced superiority with the 3D paradigm, succeeded by the GEP nonlinear paradigm and culminated with the HM linear model. The 3D paradigm was characterized by a robust Q2 (0.533), R2 (0.921), and F-values (132.338) complemented by a minimal SEE (0.110). The molecular descriptor MNO coupled with the hydrogen bond donor field facilitated novel pharmaceutical conceptualizations, leading to the identification of the quintessential active molecule, 24J.138, lauded for its superlative antineoplastic attributes and docking proficiency. The orchestration of bidimensional and tridimensional paradigms, synergized by innovative amalgamation of contour maps and molecular descriptors, provides novel insights and methodologies for the synthesis of glioblastoma chemotherapeutic agents.
对2-(1-(1,3,4-噻二唑-2-基)哌啶-4-基)乙二醇类似物进行二维和三维QSAR分析和分子对接研究,以确定靶向胶质母细胞瘤的有希望的候选药物
2-(1-(1,3,4-噻二唑-2-基)哌啶-4-基)乙二醇类似物代表新谷氨酰胺酶1抑制剂。其典型的抗肿瘤疗效强调了其在恶性母细胞瘤化疗中的应用前景。本研究旨在阐明2D和3D-QSAR模型,以验证比1-醇类似物的抗肿瘤功效,并描绘有利于新药物设计的最佳结构构型。启发式方法(HM)用于二维线性QSAR范式的开发,而基因表达编程(GEP)算法用于二维非线性QSAR范式的开发。同时,CoMSIA方法被用于仔细检查药物结构和效力之间的关系。我们对200种新生的抗胶质瘤e_1 -1-ol化合物进行了概念化,并通过化学描述符和分子场描述来预测它们的效力水平。具有峰值效力的药物被指定用于分子对接验证。经验模型在三维模型中表现出明显的优势,其次是GEP非线性模型,最后是HM线性模型。3D模式的特点是Q2 (0.533), R2(0.921)和f值(132.338),以及最小的SEE(0.110)。分子描述符MNO与氢键供体场的耦合促进了新的药物概念,导致鉴定了典型的活性分子24J。138,因其最高的抗肿瘤特性和对接能力而受到称赞。二维和三维范式的协调,通过创新地融合等高线图和分子描述符的协同作用,为胶质母细胞瘤化疗药物的合成提供了新的见解和方法。
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