新型苯并呋喃衍生物作为LSD1抑制剂的QSAR研究  

QSAR Study of Novel Benzofuran Derivatives as Potent LSD1 Inhibitors

作　　者：张渝琪 郭婧[1] 邱明秀 刘家妮 王艳[1] 朱春阳 赵淑芬[1] 邱文生[1] 

机构地区：[1]青岛大学附属医院肿瘤科,山东 青岛 [2]青岛市市立医院呼吸与危重症医学科,山东 青岛

出　　处：《临床医学进展》2023年第5期8769-8781,共13页Advances in Clinical Medicine

摘　　要：组蛋白赖氨酸特异性去甲基酶1 (LSD1)是一种黄素腺嘌呤二核苷酸(FAD)依赖性胺氧化酶,可特异性识别H3K4和H3K9底物,并去除其单甲基或二甲基修饰。它介导许多细胞内信号通路,与肿瘤的发生和发展密切相关。因此,开发高效、特异的LSD1抑制剂不仅有利于研究LSD1的生物学功能,而且对抗肿瘤药物的开发具有重要的科学意义。建立定量构效关系(QSAR)模型可以预测分子的物理和化学性质。在本研究中,利用基因表达编程(GEP)建立了一个具有描述符的非线性定量构效关系(QSAR)模型,并预测了一系列新型苯并呋喃化疗药物的活性。这些描述符是在CODESSA软件中计算的,并基于启发式算法从描述符池中选择。选择4个描述符来建立多元线性回归模型。获得了训练集和测试集的最佳非线性QSAR模型,相关系数分别为0.92和0.80,平均误差分别为0.07和0.60。显然,基于GEP的QSAR模型具有更好的抑制剂疗效预测稳定性。这些发现对LSD1抑制剂作为高选择性的一流临床候选药物的设计提供了新的价值。Histone lysine specific demethylase 1 (LSD1) is a flavin adenine dinucleotide (FAD) dependent amine oxidase, which can specifically recognize H3K4 and H3K9 substrates and remove their monomethyl or dimethyl modifications. It mediates many intracellular signal pathways and is closely related to the occurrence and development of tumors. Therefore, the development of effi-cient and specific LSD1 inhibitors is not only conducive to the study of the biological function of LSD1, but also has important scientific significance for the development of anti-tumor drugs. Estab-lishing a quantitative structure-activity relationship (QSAR) model can predict the physical and chemical properties of molecules. In this study, gene expression programming (GEP) was used to build a nonlinear quantitative structure activity relationship (QSAR) model with descriptors and to predict the activity of a series of novel DNA-targeted chemotherapeutic agents. These descriptors were calculated in CODESSA software and selected from the descriptor pool based on heuristics. Four descriptors were selected to establish a multiple linear regression model. The best nonlinear QSAR model with a correlation coefficient of 0.92 and 0.80 and mean error of 0.07 and 0.60 for the training and test sets were obtained. It is apparent that the QSAR model based on GEP has better forecasting stability of inhibitor efficacy. These findings should be useful for the design of LSD1 in-hibitors as highly selective first-in-class clinical candidate.

关 键 词：组蛋白赖氨酸特异性去甲基酶1 (LSD1) 定量构效关系(QSAR) 启发式算法 基因表达编程(GEP) 

分 类 号：R73[医药卫生—肿瘤]