Monte Carlo Method Based QSAR Studies of Mer Kinase Inhibitors in Compliance with OECD Principles

Abstract

Monte Carlo method based QSAR studies for inhibitors of Mer kinase, a potential novel target for cancer treatment, has been carried out using balance of correlation technique. The data was divided into three random and dissimilar splits and hybrid optimal descriptors derived from SMILES and hydrogen filled graphs based notations were used for construction of QSAR models. The generated models have good fitting ability, robustness, generalizability and internal predictive ability. The external predictive ability has been tested using multiple criteria and described models exhibited good performance in all of these tests. The values of R, Q, R, Q, R and ∆R for the best model are 0.9502, 0.9388, 0.9469, 0.9083, 0.7534 and 0.0894 respectively. Also, the structural characteristics responsible for enhancement and reduction of activity have been extracted. Further, the agreement with the OECD rules for QSAR model has been discussed.

QSPR/QSAR: State-of-Art, Weirdness, the Future

“…Method 1 is a one-variable model calculated with the Monte Carlo technique [61][62][63][64] for hybrid optimal descriptors, which are calculated by simplified molecular input-line entry system (SMILES) [65], together with a molecular graph [66][67][68][69][70][71]:…”

QSAR Models for Nitrogen Containing Monophosphonate and Bisphosphonate Derivatives as Human Farnesyl Pyrophosphate Synthase Inhibitors Based on Monte Carlo Method

“…Descriptors procured from either SMILES or molecular graph can be applied to symbolize the molecular structure. Literature survey reveals that "hybrid" demonstration of the molecular structure, i. e., by SMILES along with molecular graph, can grant a better model demonstrated by higher statistical quality than the model which is predicted by only SMILES or molecular graph [48], [70]. The hybrid optimal descriptor DCW, adopted for generating QSAR models for the pIC50, was determined as per the following equation:…”

“…Recent published papers reveal that the simplified molecular input line entry system (SMILES) is a substitute to classical QSAR methods and it can be used for the prediction of molecular structures with appropriate end point or activity [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49]. In all the QSAR models, depending on Monte Carlo optimization method, the pertinent activity is treated as random event [50][51][52][53].…”

“…In all the QSAR models, depending on Monte Carlo optimization method, the pertinent activity is treated as random event [50][51][52][53]. In the light of these facts and in continuation of our work [48,[54][55][56][57][58][59][60][61][62][63] on biological important heterocyclic compounds, we herein report Monte Carlo method based QSAR studies of 73 compounds i. e. thienopyrimidine-based monophosphonate (ThP-MP) and N-containing bisphosphonates N-BPs active against hFPPS using SMILES and graph optimal descriptors.…”

The Monte Carlo approach to model and predict the melting point of imidazolium ionic liquids using hybrid optimal descriptors

“…The CORAL (CORrelation And Logic) programme (available at http://www.insilico.eu/coral) has been recommended as a tool for doing QSPR analysis on a variety of endpoints. [14][15][16][17][18][19] The simplied molecular input line-entry system (SMILES) notations of the chemical structures are used to compute the descriptor correlation weight (DCW) in the CORAL soware using Monte Carlo optimization. [20][21][22][23] In recent times, many publications utilized the 'index of ideality of correlation (IIC)' as a unique criterion to construct the best predictive QSPR models.…”