HQSAR and random forest-based QSAR models for anti-T. vaginalis activities of nitroimidazoles derivatives

Veríssimo, Gabriel Corrêa; Dutra, Evaldo Francisco Menezes; Dias, Anna Letícia Teotonio; Fernandes, Philipe Oliveira; Kronenberger, Thales; Gomes, Maria Aparecida; Maltarollo, Vinícius Gonçalves

doi:10.1016/j.jmgm.2019.04.007

Cited by 32 publications

(16 citation statements)

References 31 publications

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“…HQSAR (Holographic QSAR) converts the molecular structure into characteristic molecular fingerprints according to the types of molecular fragments and marks with numbers (53-401) [32] . These marked numbers are used as QSAR descriptors to establish the corresponding structure-activity relationship to predict the biological activity of the compounds.…”

Section: Methodsmentioning

confidence: 99%

Computational strategies towards developing novel SARS-CoV-2 Mpro inhibitors against COVID-19

Luo

Tong

Zhang

et al. 2022

Journal of Molecular Structure

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The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains to be a serious threat due to the lack of a specific therapeutic agent. Computational methods are particularly suitable for rapidly fight against SARS-CoV-2. This present research aims to systematically explore the interaction mechanism of a series of novel bicycloproline-containing SARS-CoV-2 M pro inhibitors through integrated computational approaches. We designed six structurally modified novel SARS-CoV-2 M pro inhibitors based on the QSAR study. The four designed compounds with higher docking scores were further explored through molecular docking, molecular dynamics (MD) simulations, free energy calculations, and residual energy contributions estimated by the MM-PBSA approach, with comparison to compound 23(PDB entry 7D3I). This research not only provides robust QSAR models as valuable screening tools for the development of anti-COVID-19 drugs, but also proposes the newly designed SARS-CoV-2 M pro inhibitors with nanomolar activities that can be potentially used for further characterization to treat SARS-CoV-2 virus.

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Section: Methodsmentioning

confidence: 99%

Computational strategies towards developing novel SARS-CoV-2 Mpro inhibitors against COVID-19

Luo

Tong

Zhang

et al. 2022

Journal of Molecular Structure

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“…All models were constructed using similar strategies previously applied [ 53 , 77 ]. Python library Scikit-learn was used for the data analysis [ 78 ] (Fig.…”

Section: Methodsmentioning

confidence: 99%

“…The tree-based models proposed by Breiman et al . in 1984 [ 51 ] are good examples of machine learning algorithms for drug design purposes [ 52 , 53 ]. In these models, the goal is to split the dataset into binary groups with the highest possible homogeneity.…”

Section: Introductionmentioning

confidence: 99%

Molecular insights on ABL kinase activation using tree-based machine learning models and molecular docking

et al. 2021

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Graphic abstract Abelson kinase (c-Abl) is a non-receptor tyrosine kinase involved in several biological processes essential for cell differentiation, migration, proliferation, and survival. This enzyme's activation might be an alternative strategy for treating diseases such as neutropenia induced by chemotherapy, prostate, and breast cancer. Recently, a series of compounds that promote the activation of c-Abl has been identified, opening a promising ground for c-Abl drug development. Structure-based drug design (SBDD) and ligand-based drug design (LBDD) methodologies have significantly impacted recent drug development initiatives. Here, we combined SBDD and LBDD approaches to characterize critical chemical properties and interactions of identified c-Abl's activators. We used molecular docking simulations combined with tree-based machine learning models—decision tree, AdaBoost, and random forest to understand the c-Abl activators' structural features required for binding to myristoyl pocket, and consequently, to promote enzyme and cellular activation. We obtained predictive and robust models with Matthews correlation coefficient values higher than 0.4 for all endpoints and identified characteristics that led to constructing a structure–activity relationship model (SAR). Supplementary Information The online version contains supplementary material available at 10.1007/s11030-021-10261-z.

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“…When performing both external test set validation and LOO-CV jointly with a data set, usually, the whole data set is divided into a calibration set and a test set in order to carry out the external test set validation. After that, all the samples of the calibration set are predicted in turn so as to complete the LOO-CV. − …”

Section: Methodsmentioning

confidence: 99%

QSPR Studies on the Octane Number of Toluene Primary Reference Fuel Based on the Electrotopological State Index

Jiao

Liu

et al. 2020

ACS Omega

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The quantitative structure–property relationship (QSPR) models for predicting the octane number (ON) of toluene primary reference fuel (TPRF; blends of n-heptane, isooctane, and toluene) was investigated. The electrotopological state (E-state) index of TPRF components was computed and weight-summed to generate the quantitative descriptor of TPRF samples. The partial least squares (PLS) technique was used to build up the regression model between the ON and weight-summed E-state index of the investigated samples. The QSPR models for the research octane number (RON) and motor octane number (MON) of TPRF were built. The prediction performance of the obtained PLS models was assessed by the external test set validation and leave-one-out cross-validation. The validation results demonstrate that the proposed PLS models are feasible for predicting the ON, both RON and MON, of TPRF. In addition, several other QSPR models for the ON of TPRF were developed by employing the stepwise regression and Scheffé polynomials methods, and the prediction performance of these models were compared with that of the PLS models. The comparison result shows that the proposed PLS models are slightly better than multiple linear regression models and Scheffé models. It is demonstrated that the combination of the E-state index and PLS is an easy-to-use and promising method for studying and forecasting the ON of TPRF.

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HQSAR and random forest-based QSAR models for anti-T. vaginalis activities of nitroimidazoles derivatives

Cited by 32 publications

References 31 publications

Computational strategies towards developing novel SARS-CoV-2 Mpro inhibitors against COVID-19

Computational strategies towards developing novel SARS-CoV-2 Mpro inhibitors against COVID-19

Molecular insights on ABL kinase activation using tree-based machine learning models and molecular docking

QSPR Studies on the Octane Number of Toluene Primary Reference Fuel Based on the Electrotopological State Index

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