QSAR studies of imidazo[4,5-b]pyridine derivatives as anticancer drugs using RASMS method

Tong, Jianbo; Zhao, Xiang; Zhong, Li

doi:10.1007/s00044-014-1045-6

Cited by 11 publications

(5 citation statements)

References 19 publications

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“…These data are amassing at an exponential rate with no slowing down in hindsight, which sets the stage for the utilization of machine learning in making sense of these data and translating them into useful and actionable insights. There have been extensive reports on the utilization of machine learning approaches for correlating the sequences of therapeutic peptides with their biological activity (Shi et al, 1998[ 75 ]; Nagarajan et al, 2006[ 56 ]; Alam and Khan, 2014[ 2 ]; Mohseni Bababdani and Mousavi, 2013[ 55 ]; Tong et al, 2014[ 87 ]; Li et al, 2017[ 42 ]). A review of the literature indicated that there are currently no review articles concerning the use of machine learning and quantitative structure-activity relationship (QSAR) as applied to therapeutic peptides.…”

Section: Introductionmentioning

confidence: 99%

Unraveling the bioactivity of anticancer peptides as deduced from machine learning

Shoombuatong

Schaduangrat

Nantasenamat

2018

EXCLI Journal; 17:Doc734; ISSN 1611-2156

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Section: Introductionmentioning

confidence: 99%

Unraveling the bioactivity of anticancer peptides as deduced from machine learning

Shoombuatong

Schaduangrat

Nantasenamat

2018

EXCLI Journal; 17:Doc734; ISSN 1611-2156

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“…In this study, the Gauss View 05 program were used to draw the chemical structures of 105 imidazo [4, 5-b] pyridine derivatives. These chemical structures and their IUPAC name are listed in Table S1 (see Table S1 of the Supplementary) material to this paper [51][52][53].…”

Section: Materials and Mathematical Methodsmentioning

confidence: 99%

QSPR study to predict some of quantum chemical properties of anticancer imidazo[4,5‐b]pyridine derivatives using genetic algorithm multiple linear regression and molecular descriptors

Jafari,

Momeni Isfahani,

Shafiei

et al. 2023

Int J of Quantum Chemistry

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Pyridine and its derivatives have been applied clinically for the treatment of a wide range of diseases and in the synthesis of novel drugs. In the present work, imidazo[4,5‐b]pyridine derivatives as anticancer drugs were exhibited to select the important descriptor for quantum chemical properties. Two of the fundamental thermodynamic properties are heat capacity (Cv) and entropy (S), which are important in the field of chemical kinetics and are key in the understanding and design of chemical processes involving chemical reactions. A Quantitative Structure–Property Relationship (QSPR) study was used to predict the quantum chemical properties like Cv and S of 105 imidazole derivatives using molecular descriptors and the genetic algorithm–multiple linear regression (GA–MLR). The best QSPR models were selected using criteria coefficients such as R2, R2adj, RMSE and Fisher ratio. Different internal and external validation metrics were adopted to evaluate the stability, fit and predictive power of the QSPR models. The validation results and statistical analysis show that the models possess good prediction power and robustness, and the total size (TS) and Sanderson electronegativity(RDF060e) and total information content index(TIC1) of imidazo[4,5‐b]pyridine derivatives are increasingly related to the studied properties.

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“…At present, many Aurora A compounds with different inhibitory activities have been found and show effective anticancer activities [6,7]. They involves a wide range of cancers, such as colorectal cancer, prostate cancer, ovarian cancer, breast cancer and glioma [8][9][10][11]. Therefore, Aurora A is considered to be a good marker of tumor progression and prognosis, and the inhibition of its kinase activity helps to reduce tumor invasiveness.…”

Section: Introductionmentioning

confidence: 99%

QSAR Study, Molecular Docking and Molecular Dynamic Simulation of Aurora Kinase Inhibitors Derived from Imidazo[4,5-b]pyridine Derivatives

Tian,

Tong,

Liu

et al. 2024

Molecules

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Cancer is a serious threat to human life and social development and the use of scientific methods for cancer prevention and control is necessary. In this study, HQSAR, CoMFA, CoMSIA and TopomerCoMFA methods are used to establish models of 65 imidazo[4,5-b]pyridine derivatives to explore the quantitative structure-activity relationship between their anticancer activities and molecular conformations. The results show that the cross-validation coefficients q2 of HQSAR, CoMFA, CoMSIA and TopomerCoMFA are 0.892, 0.866, 0.877 and 0.905, respectively. The non-cross-validation coefficients r2 are 0.948, 0.983, 0.995 and 0.971, respectively. The externally validated complex correlation coefficients r2pred of external validation are 0.814, 0.829, 0.758 and 0.855, respectively. The PLS analysis verifies that the QSAR models have the highest prediction ability and stability. Based on these statistics, virtual screening based on R group is performed using the ZINC database by the Topomer search technology. Finally, 10 new compounds with higher activity are designed with the screened new fragments. In order to explore the binding modes and targets between ligands and protein receptors, these newly designed compounds are conjugated with macromolecular protein (PDB ID: 1MQ4) by molecular docking technology. Furthermore, to study the nature of the newly designed compound in dynamic states and the stability of the protein-ligand complex, molecular dynamics simulation is carried out for N3, N4, N5 and N7 docked with 1MQ4 protease structure for 50 ns. A free energy landscape is computed to search for the most stable conformation. These results prove the efficient and stability of the newly designed compounds. Finally, ADMET is used to predict the pharmacology and toxicity of the 10 designed drug molecules.

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QSAR studies of imidazo[4,5-b]pyridine derivatives as anticancer drugs using RASMS method

Cited by 11 publications

References 19 publications

Unraveling the bioactivity of anticancer peptides as deduced from machine learning

Unraveling the bioactivity of anticancer peptides as deduced from machine learning

QSPR study to predict some of quantum chemical properties of anticancer imidazo[4,5‐b]pyridine derivatives using genetic algorithm multiple linear regression and molecular descriptors

QSAR Study, Molecular Docking and Molecular Dynamic Simulation of Aurora Kinase Inhibitors Derived from Imidazo[4,5-b]pyridine Derivatives

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