Development of QSRR model for hydroxamic acids using PCA-GA-BP algorithm incorporated with molecular interaction-based features - Nie, Yiming; Li, Jia; Yang, Xinying; Hou, Xuben; Fang, Hao (Frontiers in Chemistry, 2022)
As a potent zinc chelator, hydroxamic acid has been applied in the design of inhibitors of zinc metalloenzyme, such as histone deacetylases (HDACs). A series of hydroxamic acids with HDAC inhibitory activities were subjected to the QSRR (Quantitative Structure–Retention Relationships) study. Experimental data in combination with calculated molecular descriptors were used for the development of the QSRR model. Specially, we employed PCA (principal component analysis) to accomplish dimension reduction of descriptors and utilized the principal components to execute GA (genetic algorithm)-BP (error backpropagation) algorithm. Moreover, we introduced molecular interaction-based features (molecular docking scores) as a new type of molecular descriptors to represent the interactions between analytes and the mobile phase. Our results indicated that the incorporation of molecular interaction-based features significantly improved the accuracy of the QSRR model, (R2 value is 0.942, and RMSEP value is 0.276). Our study not only developed QSRR model for the prediction of retention time of hydroxamic acid in HPLC but also proved the feasibility of using molecular interaction-based features as molecular descriptors.