Cardiovascular disease (CVD) is one of the major causes of human death. Preliminary evidence indicates that the inhibition treatment of Cholesteryl Ester Transfer Protein (CETP) causes the most pronounced increase in HDL cholesterol reported so far. Merck has disclosed certain (4S,5R)-5-[3,5-bis(trifluoromethyl)phenyl]−4-methyl-1,3-oxazolidin-2-one derivatives, which show potent CETP inhibitory activity. Therefore, it would be desirable to develop computational models to facilitate the screening of these inhibitors. In the present work, quantitative structure–activity relationship (QSAR) models have been developed to predict the therapeutic potency of 108 derivatives of (4S,5R)-5-[3,5-bis(trifluoromethyl)phenyl]−4-methyl-1,3-oxazolidin-2-one: Multiple Linear Regression (MLR), Support Vector Regression (SVR) and Feedforward Neural Network using Particle Swarm Optimization (FNN-PSO). Six descriptors were selected using genetic algorithms, whereas, internal and external validation of the models was performed according to all available validation strategies. It was shown that CETP inhibitory activity is mainly governed by electronegativity, the structure of the molecule, and the electronic properties. The best results were obtained with the SVR model. The results obtained may assist in the design of new CETP inhibitors.

心血管疾病（CVD）是人类死亡的主要原因之一。初步证据表明，胆固醇酯转移蛋白（CETP）的抑制处理导致迄今为止报道的HDL胆固醇增加最为明显。默克公司披露了某些（4 S，5 R）-5- [3,5-双（三氟甲基）苯基] -4-甲基-1,3-恶唑烷-2-酮衍生物，它们具有有效的CETP抑制活性。因此，需要开发计算模型以促进这些抑制剂的筛选。在目前的工作中，定量结构-活性关系（QSAR）模型已经开发出来，可以预测（4 S，5 R）-5- [3,5-双（三氟甲基）苯基] -4-甲基-1,3-恶唑烷-2--2-：线性回归（MLR），支持向量回归（SVR）和使用粒子群的前馈神经网络优化（FNN-PSO）。使用遗传算法选择了六个描述符，而根据所有可用的验证策略对模型进行了内部和外部验证。结果表明，CETP的抑制活性主要受电负性，分子结构和电子性质的控制。使用SVR模型可获得最佳结果。获得的结果可能有助于设计新的CETP抑制剂。