Machine learning approach to discovery of small molecules with potential inhibitory action against vasoactive metalloproteases

Cañizares-Carmenate, Yudith; Mena-Ulecia, Karel; MacLeod‐Carey, Desmond; Perera-Sardiña, Yunier; Hernández-Rodríguez, Erix W.; Marrero-Ponce, Yovani; Torrens, Francisco; Castillo-Garit, Juan A.

doi:10.1007/s11030-021-10260-0

Cited by 6 publications

(4 citation statements)

References 34 publications

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“…Several strategies were developed to achieve the identification of druggable compounds against novel targets, like QSAR studies, in conjunction with molecular docking [36], with molecular docking and molecular dynamics [37,38], and even with quantum mechanics/molecular mechanics methods [39]. High performance QSAR models can be obtained by using machine learning approaches to classify the molecular descriptors of compounds from large datasets [59]. Repositioning drug candidates can be identified using drug-drug interaction networks [60]; the method even allows the ranking of compounds into simple and complex multi-pathology therapies [61].…”

Section: Discussionmentioning

confidence: 99%

3D-ALMOND-QSAR Models to Predict the Antidepressant Effect of Some Natural Compounds

et al. 2021

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The current treatment of depression involves antidepressant synthetic drugs that have a variety of side effects. In searching for alternatives, natural compounds could represent a solution, as many studies reported that such compounds modulate the nervous system and exhibit antidepressant effects. We used bioinformatics methods to predict the antidepressant effect of ten natural compounds with neuroleptic activity, reported in the literature. For all compounds we computed their drug-likeness, absorption, distribution, metabolism, excretion (ADME), and toxicity profiles. Their antidepressant and neuroleptic activities were predicted by 3D-ALMOND-QSAR models built by considering three important targets, namely serotonin transporter (SERT), 5-hydroxytryptamine receptor 1A (5-HT1A), and dopamine D2 receptor. For our QSAR models we have used the following molecular descriptors: hydrophobicity, electrostatic, and hydrogen bond donor/acceptor. Our results showed that all compounds present drug-likeness features as well as promising ADME features and no toxicity. Most compounds appear to modulate SERT, and fewer appear as ligands for 5-HT1A and D2 receptors. From our prediction, linalyl acetate appears as the only ligand for all three targets, neryl acetate appears as a ligand for SERT and D2 receptors, while 1,8-cineole appears as a ligand for 5-HT1A and D2 receptors.

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Section: Discussionmentioning

confidence: 99%

3D-ALMOND-QSAR Models to Predict the Antidepressant Effect of Some Natural Compounds

et al. 2021

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“…This study starts from a data set composed of six molecules which are represented in Fig. 1 [21,22]. These molecules were optimized by means DFT calculations at the b3lyp/ma-def2-SVP basis set implemented in Orca 4.2.1 software [23,24].…”

Section: Computational Detailsmentioning

confidence: 99%

“…In previous works, we carried out a study with 133 molecules taken from literature as possible inhibitors of Thermolysin, based on the QSAR-IN methodology [21,22]. This enzyme belongs to the M4 protein family [18], with a structural similarity in the active center with the Neutral Endopeptidase (NEP) [18,20].…”

Section: Introductionmentioning

confidence: 99%

Integral Evaluation of Neutral Endopeptidase Inhibitors as Possible Antihypertensive Agents by Means Molecular Mechanics, Free Energy Calculation and Adme-Tox Properties

Castillo-Garit,

Lamazares,

Cañizares-Carmenate

et al. 2023

J. Chil. Chem. Soc.

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The most important mechanisms in the blood pressure regulation, fluid volume and sodium-potassium balance in humans is the renin-angiotensin-aldosterone system (RAAS). This regulatory pathway plays a critical role in modulating cardiac function and vascular tone. An alteration in any of the molecules that make up this system (RAAS) could contribute to the development of arterial hypertension. In this important mechanisms, the neutral endopeptidase, also called Neprilysin (NEP) is the main enzyme for the degradation of natriuretic, therefore, it is essential proteins in controlling blood pressure. In this work, we have used docking methodology, molecular dynamics simulationa and free energy calculations method (MM-PBSA), to comprehensively evaluate the inhibitory behavior of some ligands obteined from consulted literature. The principal results obtained shown these ligands were adequately oriented in the NEP pocket. The Lig783, Lig2177, and Lig3444 compounds were those with better dynamic behavior. The energetic components that contribute to the complex's stability are the electrostatic and Van der Waals components; however, when the ADME-Tox properties were analyzed, we conclude that the best possible anti-hypertensive candidate are Lig783 and Lig3444.

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“…On the other hand, the authors decided to vary that threshold for each specific isoform and, as a result, considerable improvements were made. Another research study of a different theme was presented by Cañizares-Carmenate et al [66]. Inhibitors for vasoactive metalloproteases to treat cardiovascular conditions were discovered.…”

Section: Inhibitor Designmentioning

confidence: 99%

Machine Learning Approaches for Metalloproteins

Wang

Teo

2022

Molecules

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Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts to predict the structural and functional roles of metalloproteins. Ultimately, having a comprehensive understanding of metalloproteins will lead to tangible applications, such as designing potent inhibitors in drug discovery. Recently, there has been an acceleration in the number of studies applying machine learning to predict metalloprotein properties, primarily driven by the advent of more sophisticated machine learning algorithms. This review covers how machine learning tools have consolidated and expanded our comprehension of various aspects of metalloproteins (structure, function, stability, ligand-binding interactions, and inhibitors). Future avenues of exploration are also discussed.

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Machine learning approach to discovery of small molecules with potential inhibitory action against vasoactive metalloproteases

Cited by 6 publications

References 34 publications

3D-ALMOND-QSAR Models to Predict the Antidepressant Effect of Some Natural Compounds

3D-ALMOND-QSAR Models to Predict the Antidepressant Effect of Some Natural Compounds

Integral Evaluation of Neutral Endopeptidase Inhibitors as Possible Antihypertensive Agents by Means Molecular Mechanics, Free Energy Calculation and Adme-Tox Properties

Machine Learning Approaches for Metalloproteins

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