3D-QSAR and docking studies on pyridopyrazinones as BRAF inhibitors

Ai, Yong; Wang, Shao-Teng; Tang, Chu; Sun, Ping‐Hua; Song, Fa-Jun

doi:10.1007/s00044-010-9468-1

Cited by 17 publications

(12 citation statements)

References 17 publications

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“…Previous studies have constructed the validated QSAR models for designing new drugs against the BRAF protein (Table ). − Most of them utilized 3D descriptors calculated from comparative molecular force field and comparative molecular similarity indices analysis models. They also applied the PLS as a regression model with steric, electrostatic, hydrophobic, and hydrogen bonding between the drug and target as molecular descriptors. − The training datasets of previous studies included 27–243 compounds, while the test datasets comprised 15–189 compounds.…”

Section: Resultsmentioning

confidence: 99%

StackBRAF: A Large-Scale Stacking Ensemble Learning for BRAF Affinity Prediction

Syahid,

Weerapreeyakul,

Srisongkram

2023

ACS Omega

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The B-rapidly accelerated fibrosarcoma (BRAF) is a proto-oncogene that plays a vital role in cell signaling and growth regulation. Identifying a potent BRAF inhibitor can enhance therapeutic success in high-stage cancers, particularly metastatic melanoma. In this study, we proposed a stacking ensemble learning framework for the accurate prediction of BRAF inhibitors. We obtained 3857 curated molecules with BRAF inhibitory activity expressed as a predicted half-maximal inhibitory concentration value (pIC50) from the ChEMBL database. Twelve molecular fingerprints from PaDeL-Descriptor were calculated for model training. Three machine learning algorithms including extreme gradient boosting, support vector regression, and multilayer perceptron were utilized for constructing new predictive features (PFs). The meta-ensemble random forest regression, called StackBRAF, was created based on the 36 PFs. The StackBRAF model achieves lower mean absolute error (MAE) and higher coefficient of determination (R 2 and Q 2) than the individual baseline models. The stacking ensemble learning model provides good y-randomization results, indicating a strong correlation between molecular features and pIC50. An applicability domain of the model with an acceptable Tanimoto similarity score was also defined. Moreover, a large-scale high-throughput screening of 2123 FDA-approved drugs against the BRAF protein was successfully demonstrated using the StackBRAF algorithm. Thus, the StackBRAF model proved beneficial as a drug design algorithm for BRAF inhibitor drug discovery and drug development.

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Section: Resultsmentioning

confidence: 99%

StackBRAF: A Large-Scale Stacking Ensemble Learning for BRAF Affinity Prediction

Syahid,

Weerapreeyakul,

Srisongkram

2023

ACS Omega

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“…In 3D-QSAR experiments, the structure alignment of compounds will directly affect the establishment of subsequent 3D models, so it is very important to select appropriate structure alignment methods for compounds ( Li et al, 2005 ; Patel et al, 2008 ; Ai et al, 2011 ). Ligand-based alignment was used in this experiment ( Figure 2 ) since compound 19g was the compound with the best compound activity in the dataset, all compounds were aligned with compound 19g as a template.…”

Section: Methodsmentioning

confidence: 99%

3D,2D-QSAR study and docking of novel quinazolines as potential target drugs for osteosarcoma

Lian

Sang

et al. 2023

Front. Pharmacol.

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Background: Quinazolines are an important class of benzopyrimidine heterocyclic compounds with a promising antitumor activity that can be used for the design and development of osteosarcoma target compounds.Objective: To predict the compound activity of quinazoline compounds by constructing 2D- and 3D-QSAR models, and to design new compounds according to the main influencing factors of compound activity in the two models.Methods: First, heuristic method and GEP (gene expression programming) algorithm were used to construct linear and non-linear 2D-QSAR models. Then a 3D-QSAR model was constructed using CoMSIA method in SYBYL software package. Finally, new compounds were designed according to molecular descriptors of 2D-QSAR model and contour maps of 3D-QSAR model. Several compounds with optimal activity were used for docking experiments with osteosarcoma related targets (FGFR4).Results: The non-linear model constructed by GEP algorithm was more stable and predictive than the linear model constructed by heuristic method. A 3D-QSAR model with high Q2 (0.63) and R2 (0.987) values and low error values (0.05) was obtained in this study. The success of the model fully passed the external validation formula, proving that the model is very stable and has strong predictive power. 200 quinazoline derivatives were designed according to molecular descriptors and contour maps, and docking experiments were carried out for the most active compounds. Compound 19g.10 has the best compound activity with good target binding capability.Conclusion: To sum up, the two novel QSAR models constructed were very reliable. The combination of descriptors in 2D-QSAR with COMSIA contour maps provides new design ideas for future compound design in osteosarcoma.

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“…CoMFA and CoMSIA methods were applied to build 3D-QSAR models using the structural parameters as independent variables and the binding free energy as the dependent variable to obtain the relationship between the binding free energy and molecular structure. In construction of the 3D-QSAR model, molecular alignment is an important step [ 42 ]. In this approach, decachlorobiphenyl, which had the largest binding free energy, was used as the template to align the remaining PCBs.…”

Section: Methodsmentioning

confidence: 99%

Relationship between the binding free energy and PCBs’ migration, persistence, toxicity and bioaccumulation using a combination of the molecular docking method and 3D-QSAR

Zhao

Wang

Liu

2018

Chemistry Central Journal

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The molecular docking method was used to calculate the binding free energies between biphenyl dioxygenase and 209 polychlorinated biphenyl (PCB) congeners. The relationships between the calculated binding free energies and migration (octanol–air partition coefficients, KOA), persistence (half-life, t1/2), toxicity (half maximal inhibitory concentration, IC50), and bioaccumulation (bioconcentration factor, BCF) values for the PCBs were used to gain insight into the degradation of PCBs in the presence of biphenyl dioxygenase. The relationships between the calculated binding free energies and the molecular weights, KOA, BCF, and t1/2 values for the PCBs were statistically significant (P < 0.01), whereas the relationship between the calculated binding free energies and the IC50 for the PCBs was not statistically significant (P > 0.05). The electrostatic field, derived from three-dimensional quantitative structure–activity relationship studies, was a primary factor governing the binding free energy, which agreed with literature findings for KOA, t1/2, and BCF. Comparative molecular field analysis and comparative molecular similarity indices analysis contour maps showed that the binding free energies, KOA, t1/2, and BCF values for the PCBs decreased simultaneously when substituents with electropositive groups at the 3-position or electronegative groups at the 3′-position were introduced. This indicated the binding free energy was correlated with the persistent organic pollutant characteristics of PCBs. Furthermore, low binding free energies improved the degradation of the PCBs and simultaneously decreased the KOA, t1/2, and BCF values, thereby reducing the persistent organic pollutant characteristics of PCBs in the environment. These results are expected to be beneficial in providing a theoretical foundation for further elucidation of the degradation and molecular modification of PCBs.Electronic supplementary materialThe online version of this article (10.1186/s13065-018-0389-2) contains supplementary material, which is available to authorized users.

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3D-QSAR and docking studies on pyridopyrazinones as BRAF inhibitors

Cited by 17 publications

References 17 publications

StackBRAF: A Large-Scale Stacking Ensemble Learning for BRAF Affinity Prediction

StackBRAF: A Large-Scale Stacking Ensemble Learning for BRAF Affinity Prediction

3D,2D-QSAR study and docking of novel quinazolines as potential target drugs for osteosarcoma

Relationship between the binding free energy and PCBs’ migration, persistence, toxicity and bioaccumulation using a combination of the molecular docking method and 3D-QSAR

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