Development of QSRR model for hydroxamic acids using PCA-GA-BP algorithm incorporated with molecular interaction-based features

Abstract

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 of compounds (16 training compounds, 4 validation compounds and 7 test compounds) to execute GA (genetic algorithm)-BP (error backpropagation) algorithm. We performed double cross-validation approach for obtaining a more convincing model. Moreover, we introduced molecular interaction-based features (molecular docking scores) as a new type of molecular descriptor 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 0.842, RMSEP value is 0.440, and MAE value is 0.573). Our study not only developed QSRR model for the prediction of the retention time of hydroxamic acid in HPLC but also proved the feasibility of using molecular interaction-based features as molecular descriptors.

Simultaneous effect of different chromatographic conditions on the chromatographic retention of pentapeptide derivatives (HGRFG and NPNPT)

“…Oligopeptide separations by reversed-phase high-performance liquid chromatography (RP-HPLC) are extremely common ( Ofosu et al, 2021 ; Samtiya et al, 2021 ; Waili et al, 2021 ), and the chromatographic retention of oligopeptides in RP-HPLC is driven by hydrophobic interactions ( Sousa et al, 2021 ). Combining the molecular structure of compounds with the parameters describing the properties of chromatographic mobile and stationary phases, functional relationships could be obtained ( Nie et al, 2022 ). These relationships can be used to analyze and predict the chromatographic behavior of other compounds ( Janicka et al, 2020 ; Nie et al, 2022 ) and evaluate the pharmacokinetics and biochemical properties of drugs, such as absorption, distribution, metabolism, and excretion (ADME) in vivo ( Langyan et al, 2021 ).…”

“…Combining the molecular structure of compounds with the parameters describing the properties of chromatographic mobile and stationary phases, functional relationships could be obtained ( Nie et al, 2022 ). These relationships can be used to analyze and predict the chromatographic behavior of other compounds ( Janicka et al, 2020 ; Nie et al, 2022 ) and evaluate the pharmacokinetics and biochemical properties of drugs, such as absorption, distribution, metabolism, and excretion (ADME) in vivo ( Langyan et al, 2021 ). It can also preliminarily determine the solubility, lipophilicity, bioaccumulation, and toxicity of compounds in vivo , which is of great significance in the field of new drug molecule development, especially in the analysis of the chemical properties of peptides in vivo .…”