Topological Distance Based 3D Descriptors for Use in QSAR and Diversity Analysis

Abstract

In topological autocorrelation approaches molecular descriptors are calculated by summing up properties located at given topological distances. Since the relationship between topological and Euclidean distance contains 3D structural information, in the present paper a modified version of an autocorrelation approach is proposed to include this type of information. Steric, electronic, and indicator-variable-type descriptors are calculated and used in QSAR studies with three different data sets. The results demonstrate that the descriptors can be efficiently used in cluster- and QSAR analysis. The models obtained are highly predictive and comparable to those obtained by other commonly used 3D-QSAR methods.

Applying pattern recognition methods plus quantum and physico‐chemical molecular descriptors to analyze the anabolic activity of structurally diverse steroids

“…The dataset of steroids, studied more extensively with different methods by several research groups, is the steroid benchmark with the corresponding globulin affinity. [15][16][17] Recently, we reported QSAR models for congeneric series of AASs: 17-hydroxy-5-androstane, 18 4,5-dihydrotestosterone 19 and testosterone 20 steroid families.…”

Use Of The R-Group Descriptor for Alignment-Free QSAR

“…We define an R-group, i, in terms of a specific assumed to be at a distance of n bonds from the point-of-attachment. This representation of molecular structure is similar in concept to the descriptor introduced by Martin et al [9] for the design of structurally diverse combinatorial libraries, and is also related to the autocorrelation functions that have been reported by several workers [10][11][12].…”

Novel global and local 3D atom-based linear descriptors of the Minkowski distance matrix: theory, diversity–variability analysis and QSPR applications

“…One of the approaches for overcoming this problem is by using a 3D model from Cartesian coordinates, which is invariant to rotation and translation of the molecule and alignment-independent (free) [24,30]. The MS-WHIM (weighted holistic invariant molecular) (and also WHIM descriptors [31]) approach overcomes the problem of molecular alignment by calculating statistical parameters (eigenvalue proportion, skewness and kurtosis) from the score matrix obtained from weighted principal component analysis (PCA) [32].…”