Background: 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. Objectives: 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. Methods: 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. Results: 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. Conclusion: 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.

中文翻译：

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对 2-(1-(1,3,4-thiadiazol-2-yl)piperidin-4-yl)ethan-1-ol 类似物进行 2D 和 3D QSAR 分析和分子对接研究，旨在识别有前景的候选药物用于靶向胶质母细胞瘤

背景：2-(1-(1,3,4-噻二唑-2-基)哌啶-4-基)乙烷-1-醇类似物代表新型谷氨酰胺酶 1 抑制剂。它们出色的抗肿瘤功效强调了它们在胶质母细胞瘤化疗中的潜在用途。目的：本研究旨在阐明 2D 和 3D-QSAR 模型，以验证 1-ol 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配置。方法：启发式方法 (HM) 用于开发 2D 线性 QSAR 范式，而基因表达编程 (GEP) 算法用于 2D 非线性 QSAR 范式。同时，采用 CoMSIA 方法来仔细检查药物结构和效力之间的关系。概念化了 200 种新生抗神经胶质瘤 ethan-1-ol 化合物的集合，通过化学描述符和分子场描述来预测它们的效力水平。体现峰值效力的药物被指定用于分子对接验证。结果：经验模型在 3D 范式中表现出明显的优越性，随后由 GEP 非线性范式继承并最终以 HM 线性模型达到顶峰。3D 范式的特点是具有稳健的 Q2 (0.533)、R2 (0.921) 和 F 值 (132.338)，并辅以最小的 SEE (0.110)。分子描述符 MNO 与氢键供体场相结合促进了新颖的药物概念化，从而鉴定出了典型的活性分子 24J.138，该分子因其卓越的抗肿瘤特性和对接能力而受到赞誉。结论：二维和三维范式的编排，