<scp>Structure–activity</scp> relationship and <i>in silico</i> development of <scp>c‐Met</scp> kinase inhibitors

Ghosh, Suparna; Cho, Seung Joo

doi:10.1002/bkcs.12537

Cited by 5 publications

(4 citation statements)

References 43 publications

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“…In Structure-Activity Relationships (SAR), the electronic structure of reactants or products can be modified by variations in their substituents, and this can affect the energetics of the reaction. 45,46 The slope of a LFER is a measure of the sensitivity of the TS energy to changes in the electronic structure of the reactants or products. The steeper the slope, the more sensitive the TS energy is to changes in the reactants or products, indicating a stronger dependence of the TS on the electronic structure of the reactants or products.…”

Section: Resultsmentioning

confidence: 99%

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A theoretical study for the linear free energy relationship of CH bond activation and the role of the axial ligand in cytochrome P450 model complexes

Kwon,

Choi,

Kim

2024

Bulletin Korean Chem Soc

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Hydrogen abstraction is essential for CH bond activation by Compound I in cytochrome P450 and is influenced by various factors, including spin states, bond dissociation energies of the CH and FeOH bonds, axial ligands, and quantum mechanical tunneling. The role of axial ligands has been extensively studied, but it is still not fully understood. To explore their role, we used density functional theory calculations to determine whether a linear free energy relationship is established for the hydrogen transfer reaction, according to changes in axial ligands. The B3LYP* functional exhibits a strong linear correlation, but the slopes are inconsistent with the characteristics of the transition state. Natural bond orbital analysis reveals no direct orbital interaction between axial ligands and the reaction center of hydrogen transfer. The electron‐donating orbitals of the axial ligands weaken the FeO bond, lowering the energy barrier, but they do not directly participate in the intrinsic hydrogen transfer. During the reaction, the FeO bond length increases significantly before the hydrogen transfer itself, generating an asynchronous shift in the bond orders, and most of the activation energy is used for the increase in the FeO bond rather than the hydrogen transfer itself. This study may explain why there is no apparent correlation between the rate constants and the FeO bond strength.

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Section: Resultsmentioning

confidence: 99%

“…In Structure–Activity Relationships (SAR), the electronic structure of reactants or products can be modified by variations in their substituents, and this can affect the energetics of the reaction 45,46 . The slope of a LFER is a measure of the sensitivity of the TS energy to changes in the electronic structure of the reactants or products.…”

Section: Resultsmentioning

confidence: 99%

A theoretical study for the linear free energy relationship of CH bond activation and the role of the axial ligand in cytochrome P450 model complexes

Kwon,

Choi,

Kim

2024

Bulletin Korean Chem Soc

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“…According to our previous study, 26 the binding free energy (

∆ G_{MM - GBSA}

) and entropy term (– T Δ S ) as interaction entropy (IE) were calculated using the gmx_MMPBSA package 27 . The binding free energy from the MM‐GBSA calculation can be written as:

∆ G_{MM - GBSA} = E_{RL} - E_{normalR} - E_{normalL}

where the free energy of the complex (RL), receptor (R), and ligand (L) are represented by

E_{RL}

E_{normalR}

, and

E_{normalL}

respectively.…”

Section: Methodsmentioning

confidence: 99%

“…According to our previous study, 26 the binding free energy (ΔG MMÀGBSA ) and entropy term (-TΔS) as interaction entropy (IE) were calculated using the gmx_MMPBSA package. 27 The binding free energy from the MM-GBSA calculation can be written as:…”

Section: Simulation and Binding Energy Calculationsmentioning

confidence: 99%

Comparative binding affinity analysis of dual CDK2/FLT3 inhibitors

Ghosh

Cho

2022

Bulletin Korean Chem Soc

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Selective inhibition of cyclin‐dependent kinase 2 (CDK2) using small molecules is gaining popularity for the treatment of certain types of acute myeloid leukemia (AML). In this study, we used different molecular modeling techniques to investigate the structure–activity relationship (SAR) and binding modalities of dual CDK2/FLT3 inhibitors. The key chemical characteristics of the 3H‐pyrazolo[4,3‐f]quinoline derivatives were highlighted as descriptive colored contours using comparative molecular similarity analysis (CoMSIA). Modifying chemical groups in existing compounds along these contours could improve CDK2 selectivity over FMS‐like tyrosine kinase 3 (FLT3). We determined the ligand affinities for CDK2 by estimating the binding free energy using molecular mechanics generalized Born surface area (MM‐GBSA) and umbrella sampling (US) simulations. Reasonable correlations were found between the computed and experimental binding energies, suggesting that MM‐GBSA and US can be used to reliably predict the binding affinities of new compounds in the more potent CDK2 drug development process.

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Leveraging class-balancing techniques for predicting c-MET Inhibitors: Descriptor Calculation, Selection, and QSAR Model Optimization using Machine Learning

Mishra,

Acharya,

Pandit

et al. 2024

Preprint

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The rapid emergence of resistance in cancer chemotherapy is a major challenge in the drug discovery of cancer, restricting the action of various important classes of inhibitors against EGFR, VEGF, BRAF, alkylating agents, and DNA damaging agents. c-MET plays an important role in the development of resistance to cancer. Identifying a potent c-MET inhibitor can improve therapeutic access to existing anti-cancer agents. In the current study, we propose a novel technique for the prediction of drug activity class by using class balancing and ML classifiers. This study utilizes 3091 molecules with c-MET inhibitory concentration value (IC50) publicly available from the ChEMBL Database. Using 14 descriptors and random oversampling for class balancing, we investigated seven classical ML models, i.e., decision tree (DT), Adaboost decision tree (ABDT), K-nearest neighbors (K-NN), support vector machine (SVM), Bernoulli Naïve Bayes (BNB), random forest (RF), and linear logistic regression (LLR) for activity prediction against c-MET. Of which SVM, LR, and RF were the top three models providing high predictability after applying balancing techniques and performing rigorous.hyperparameter tuning. Even though SVM, LR, and RF showed exemplary performance in terms of ROC-AUC and recall metrics, their validation on seven FDA-approved drugs demonstrated their susceptibility to high false negatives. Owing to this, we developed a consensus mechanism based on these three models. The consensus mechanism can work on a large, diverse database and screen potential inhibitors, prioritizing which molecule to be considered further for experimental testing. The consensus model proved beneficial as a drug design algorithm for c-MET inhibitor drug discovery and development.

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Structure–activity relationship and in silico development of c‐Met kinase inhibitors

Cited by 5 publications

References 43 publications

A theoretical study for the linear free energy relationship of CH bond activation and the role of the axial ligand in cytochrome P450 model complexes

A theoretical study for the linear free energy relationship of CH bond activation and the role of the axial ligand in cytochrome P450 model complexes

Comparative binding affinity analysis of dual CDK2/FLT3 inhibitors

Leveraging class-balancing techniques for predicting c-MET Inhibitors: Descriptor Calculation, Selection, and QSAR Model Optimization using Machine Learning

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