In Silico Analysis of FDA Drugs as P2X4 Modulators for the Treatment of Alcohol Use Disorder

Reyes-Espinosa, Francisco; Nieto-Pescador, María G.; Bocanegra-García, Virgilio; Guzmán, Eduardo Lozano; Rivera, Gildardo

doi:10.1002/minf.201900111

Cited by 2 publications

(2 citation statements)

References 35 publications

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“… Reyes-Espinosa et al. (2020) conducted a screening campaign to evaluate the potential positive allosteric modulator (PAM) activity on P2X4 of 1,657 drugs approved by the Food and Drug Administration (FDA).…”

Section: Molecular Docking and Virtual Screeningmentioning

confidence: 99%

“…(2020) conducted a screening campaign to evaluate the potential positive allosteric modulator (PAM) activity on P2X4 of 1,657 drugs approved by the Food and Drug Administration (FDA). They evaluated the activity of these drugs in four different species (human, mouse, rat, and zebrafish) and identified nine molecules with PAM activity and eight as potential negative allosteric modulators (NAM) ( Reyes-Espinosa et al., 2020 ). Caseley et al.…”

Section: Molecular Docking and Virtual Screeningmentioning

confidence: 99%

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Molecular Modeling Applied to the Discovery of New Lead Compounds for P2 Receptors Based on Natural Sources

et al. 2020

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P2 receptors are a family of transmembrane receptors activated by nucleotides and nucleosides. Two classes have been described in mammals, P2X and P2Y, which are implicated in various diseases. Currently, only P2Y12 has medicines approved for clinical use as antiplatelet agents and natural products have emerged as a source of new drugs with action on P2 receptors due to the diversity of chemical structures. In drug discovery, in silico virtual screening (VS) techniques have become popular because they have numerous advantages, which include the evaluation of thousands of molecules against a target, usually proteins, faster and cheaper than classical high throughput screening (HTS). The number of studies using VS techniques has been growing in recent years and has led to the discovery of new molecules of natural origin with action on different P2X and P2Y receptors. Using different algorithms it is possible to obtain information on absorption, distribution, metabolism, toxicity, as well as predictions on biological activity and the lead-likeness of the selected hits. Selected biomolecules may then be tested by molecular dynamics and, if necessary, rationally designed or modified to improve their interaction for the target. The algorithms of these in silico tools are being improved to permit the precision development of new drugs and, in the future, this process will take the front of drug development against some central nervous system (CNS) disorders. Therefore, this review discusses the methodologies of in silico tools concerning P2 receptors, as well as future perspectives and discoveries, such as the employment of artificial intelligence in drug discovery.

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Section: Molecular Docking and Virtual Screeningmentioning

confidence: 99%

Section: Molecular Docking and Virtual Screeningmentioning

confidence: 99%

Molecular Modeling Applied to the Discovery of New Lead Compounds for P2 Receptors Based on Natural Sources

et al. 2020

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Detection of anemic condition in patients from clinical markers and explainable artificial intelligence

Darshan,

Sampathila,

Bairy

et al. 2024

THC

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BACKGROUND: Anaemia is a commonly known blood illness worldwide. Red blood cell (RBC) count or oxygen carrying capability being insufficient are two ways to describe anaemia. This disorder has an impact on the quality of life. If anaemia is detected in the initial stage, appropriate care can be taken to prevent further harm. OBJECTIVE: This study proposes a machine learning approach to identify anaemia from clinical markers, which will help further in clinical practice. METHODS: The models are designed with a dataset of 364 samples and 12 blood test attributes. The developed algorithm is expected to provide decision support to the clinicians based on blood markers. Each model is trained and validated on several performance metrics. RESULTS: The accuracy obtained by the random forest, K nearest neighbour, support vector machine, Naive Bayes, xgboost, and catboost are 97%, 98%, 95%, 95%, 98% and 97% respectively. Four explainers such as Shapley Additive Values (SHAP), QLattice, Eli5 and local interpretable model-agnostic explanations (LIME) are explored for interpreting the model predictions. CONCLUSION: The study provides insights into the potential of machine learning algorithms for classification and may help in the development of automated and accurate diagnostic tools for anaemia.

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In Silico Analysis of FDA Drugs as P2X4 Modulators for the Treatment of Alcohol Use Disorder

Cited by 2 publications

References 35 publications

Molecular Modeling Applied to the Discovery of New Lead Compounds for P2 Receptors Based on Natural Sources

Molecular Modeling Applied to the Discovery of New Lead Compounds for P2 Receptors Based on Natural Sources

Detection of anemic condition in patients from clinical markers and explainable artificial intelligence

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