Conformation‐Independent QSAR Study on Human Epidermal Growth Factor Receptor‐2 (HER2) Inhibitors

Abstract

Inhibition of HER2 (human epidermal growth factor receptor 2) expression and function is required in several cancer treatments. Numerous compounds with very different molecular structures have been suggested as HER2 inhibitors. Here we perform quantitative structure‐activity relationship (QSAR) analysis on 444 of such compounds to investigate the molecular properties that may influence its efficiency. Models based on 1D and 2D flexible molecular descriptors are proposed to develop simple models based solely on constitutional and topological molecular features. A large number of non‐conformational descriptors (17974) was used to thoroughly explore the structural characteristics that influence the HER2 inhibitory activity. Three different approaches were explored using: 1) Molecular Descriptors, 2) Flexible Molecular Descriptors, and 3) Hybrid Descriptors. A QSAR model for HER2 inhibitors was successfully developed. Some properties such as electronegativity, aromatic character, and the presence of amino groups appear as molecular characteristics that may have influence in the HER2 inhibitory activity.

Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors

“…The dataset partitioning has to achieve similar structure-activity relationships in the three subsets; in other words, the training set molecules should be representative of the validation and test set compounds. For this purpose, the split of the dataset was carried out by means of the balanced subsets method (BSM) [ 37 , 38 ], a procedure proposed by our group that ensures that balanced subsets are generated. The BSM is based on the k-means cluster analysis (k-MCA) method [ 39 ]: the essence of k-MCA is to create k-clusters or groups of compounds in such a way that compounds in the same cluster are very similar in terms of distance metrics (i.e., Euclidean distance), and compounds in different clusters are very distinct.…”

Quantitative structure–activity relationship (QSAR) analysis of plant‐derived compounds with larvicidal activity against Zika Aedes aegypti (Diptera: Culicidae) vector using freely available descriptors

“…Currently, many tools for selecting molecular descriptors have been reported in QSAR studies. Among the linear techniques used to search for the best descriptors from a great number of variables, the RM employed here has been successfully applied in many QSAR studies . The RM technique is an efficient tool that generates an MLR model on a training set, by searching in a pool with a number of D descriptors for an optimal subset having a number of d descriptors ( D is much larger than d ) with the smallest standard deviation ( S train ) or smallest root mean square (RMS train ) .…”

“…Simultaneously, we employed the replacement method (RM) variable subset selection technique based on multivariable linear regression (MLR) . In recent years, this technique has been used to obtain a suitable structural description in the most meaningful QSAR models …”

In silico evaluation and docking studies of pyrazole analogs as potential autophagy modulators against pancreatic cancer cell line MIA PaCa-2

“…Quantitative structure activity relationships is a mathematical equation relating chemical structure with its physical, chemical and biological effect [14], QSAR model is useful for understanding the factors controlling activity and for designing new compounds for therapeutic areas [15][16][17], it requires a compound set that has been tested against an identified molecular target, cell tissue, or even microorganism, under the same experimental conditions and possesses the minimum variance in the observed responses [18]. Once a suitable dataset has been selected, the main step of modeling requires molecular/physicochemical properties, followed by variable selection, model generation from different algorithms and validation process using internal and external dataset [19][20][21].…”