Fructose-1,6-bisphosphatase (FBPase) is an enzyme important for regulation of gluconeogenesis, which is a major process in the liver responsible for glucose production. Inhibition of FBPase enzyme causing blockage of the gluconeogenesis process represents a newer scheme in the progress of anti-diabetic drugs. The current research describes the development of hybrid optimal descriptors-based quantitative structure–activity relationship (QSAR) models intended for a set of 62 FBPase inhibitors with the Monte Carlo method. The molecular structures were expressed by the simplified molecular input line entry system (SMILES) notation. Three splits were prepared by random division of the molecules into training set, calibration set and validation set. Statistical parameters obtained from QSAR modelling were good for various designed splits. The best QSAR model showed the following parameters: the values of r² for calibration set and validation set of the best model were 0.6837 and 0.8623 and of Q² were 0.6114 and 0.8036, respectively. Based on the results obtained for correlation weights, different structural attributes were described as promoter of the endpoint. Further, these structural attributes were used in designing of new FBPase inhibitors and a molecular docking study was completed for the determination of interactions of the designed molecules with the enzyme.

1,6-果糖双磷酸酶（FBPase）是一种对调节糖异生的重要酶，糖异生是肝脏中负责葡萄糖生成的主要过程。抑制FBPase酶引起糖异生过程的阻断代表了抗糖尿病药物进展中的一种新方案。当前的研究描述了基于蒙特卡洛方法的混合最优基于描述符的定量结构-活性关系（QSAR）模型的开发，该模型旨在用于一组62种FBPase抑制剂。分子结构由简化的分子输入线输入系统（SMILES）表示。通过将分子随机分为训练集，校准集和验证集来制备三个拆分。从QSAR建模获得的统计参数对于各种设计的分割都很好。最佳模型的校准集和验证集的r ²分别为0.6837和0.8623，Q ²的r ²分别为0.6114和0.8036。根据获得的相关权重结果，将不同的结构属性描述为端点的启动子。此外，这些结构属性被用于设计新的FBPase抑制剂，并且完成了分子对接研究，以确定所设计的分子与酶的相互作用。