QSAR models for 1,2,4-benzotriazines as Src inhibitors based on Monte Carlo method

Abstract

A series of 53 1,2,4-benzotriazines as inhibitors of the sarcoma family of protein tyrosine kinases have been studied. The Monte Carlo method has been used as a tool to build up the quantitative structure-activity relationships for appropriate inhibition activity. The QSAR models were calculated with the representation of the molecular structure by the simplified molecular input-line entry system. Three various splits into training and test sets have been examined. The statistical quality of all build models is very good. The best calculated model had following statistical parameters: r 2 = 0.9843, q 2 = 0.9831 for the training set and r 2 = 0.9488, q 2 = 0.9300 for the test set. The structural indicators (alerts) for the increase and decrease in the inhibition activity have been defined. The computer-aided design of new potential sarcoma inhibitor derivatives has been presented by using defined structural alerts.

Monte Carlo Method‐Based QSAR Modeling of Penicillins Binding to Human Serum Proteins

“…The correlation weights (CW) of molecular features (SA k ) calculated by using CORAL software for SMILES‐based descriptors can be used for the classification of these features according to their values from three Monte Carlo optimization runs. According to their values, SA k s can be classified into three categories: SA k with the stable positive values of CW – the promoters of the increase of an endpoint value; SA k with the stable negative values of CW – the promoters of the decrease of an endpoint value; and unstable SA k , which have both positive and negative values of CW for three Monte Carlo optimization runs . This means that if the CW(SA k ) is >0 in all three runs of the Monte Carlo optimization process, then the SA k is the promoter of Ac increase; if CW(SA k ) is <0 in all three runs of the optimization, then the SA k is the promoter of Ac decrease.…”

Use of the Monte Carlo Method for OECD Principles‐Guided QSAR Modeling of SIRT1 Inhibitors

“…The QSAR model has to be validated both internally as well as externally to confirm the robustness, stability, reliability, and predictivity of the developed model . For assessment of the robustness, reliability, and predictive ability of the model, we employed the following statistical criteria in this work.…”

Monte Carlo method based QSAR modeling of maleimide derivatives as glycogen synthase kinase-3β inhibitors

“…Test set Monte Carlo optimization runs is positive then that SA k is the promoter of increase; if CW(SA k ) from three independent Monte Carlo optimization runs is negative then that SA k is the promoter of decrease; if there are both positive and negative values of CW (Sk) in three runs of the Monte Carlo optimization process, then that SA k has an undefined role [26][27][28][29]32,33]. The list of all SA k , with the correlation weights for three runs of the Monte Carlo optimization process of the built QSAR model for maleimide derivatives is shown in Table S3.…”

“…A SMILES notation based optimal descriptor is a molecular descriptor which depends both on the molecular structure and the property under analysis, but does not explicitly depend on details from the 3D-molecular geometry. The development of QSAR models, where the SMILES is the representation of the molecular structure and is used for developing optimal descriptors, is an attractive direction of research work in the field of the QSAR theory and applications [32][33][34]. Previous QSPR/QSAR studies have shown the importance of this methodology, which was capable of developing models with a comparable or sometimes better quality to the ones built with descriptors in a pool containing thousands of 0D-3D descriptors [35][36][37][38][39][40][41][42].…”