Three-dimensional holographic vector of atomic interaction field for quantitative structure-retention relationship of purine bases

Abstract

A newly developed descriptor, threedimensional holographic vector of atomic interaction field (3D-HoVAIF), was used to describe the chemical structures of purine bases. After variable screening by stepwise multiple regression (SMR) technique, a partial least square (PLS) regression model was built with 3D-HoVAIF. The model was satisfactory comparing to reference since correlation coefficients of molecular modeling ( ), cross-validation ( ) and standard deviation of estimation (SD) were 0.966, 0.860 and 0.112, respectively, showing that the model had favorable estimation and prediction capabilities. It was illustrated that information related to retention data of purine bases could preferably be expressed by 3D-HoVAIF with definite physicochemical meanings and easy structural interpretation for purine bases. It was illustrated that 3D-HoVAIF was to preferably express retention data of purine bases and had definite physicochemical significance. So 3D-HoVAIF was a useful structural expression technique for quantitative structure activity (or property or retention) relationships (QSAR/QSPR/QSRR) study, such as structural characterization and chromatographic retention prediction. 2 cum R 2 cum Q Keywords: three-dimensional holographic vector of atomic interaction field (3D-HoVAIF), quantitative structure-retention relationship (QSRR), purine bases.It is necessary to introduce structural descriptors when creating quantitative structure-retention relationship. In the past significant work has been done for QSRR [1,2] researches, for retention index predictions, separation condition selections and retention mechanism exploration [3] . For example, QSRR models [4][5][6][7][8][9][10] were established by introducing such descriptors as molecular geometrical characteristics, topological structures and diversities of physicochemical parameters. However, it was difficult to construct QSRR model for organic and biological molecules because they were based on 2D structures without considering interactions among compounds, fixed phase and fluxion phase. Under such circumstances, 3D descriptors have been gradually developed in structural representation. Recently, there have been only few reports on 3D descriptors. Gramatica [11] used weighted holistic invariant molecular index (WHIM) proposed by Todeschini [12][13][14] to calculate chromatogram retention index of polychlorobiphenyl and Luo [15] used comparative molecular field analysis (CoMFA) proposed by Cramer [16] to calculate purine bases' capacity factor. Since WHIM index arises from rotation and translation of constants via weighted transform of physical variables, the calculation process is too intricate to be generalized. While CoMFA requires a series of complex procedures, such as overlap of molecular steric structure and configuration, partition of spatial grids, control of variable amounts and selection of potential probes. Combinating advantages of both 3D methods in explicit, accurate and explainable representation and 2D methods in easy and quick characterization, 3D...

QSPR Study on the Binding Constants of Coumarins and Human Serum Albumin

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Three‐Dimensional Holographic Vector of Atomic Interaction Field Applied in QSAR of Anti‐HIV HEPT Analogues

“…Besides, some other issues are also inevitable as partition of spatial lattice, control of variable amounts, and selection of appropriate probes in potential fields. Seeing that, a novel 3D Molecular Structural Characterization (MSC) method, Three-Dimensional Holographic Vector of Atomic Interaction Field (3D-HoVAIF) [20,21] is proposed in our laboratory, including merits of both traditional 2D and 3D descriptors (2D is easy and rapid for calculations and 3D is more amenable to physicochemical interpretations). 3D-HoVAIF method derives multi-dimensional vectors to represent molecular steric structural characteristics, is independent of experiments and does not require conformation alignment, which was used to express drug structure and activity of HEPT analogues together with Partial Least Square (PLS) or Support Vector Machine (SVM) and generates a good result.…”