Natriuretic effect of 4',5-dihydroxy-6,7-methylenedioxyflavonol-3--α-L-rhamnopyranosyl-(1→2)-β-D-xylopyranoside from and studies.

Aqueous leaf extracts of are widely used because of their diuretic, natriuretic, antiurolithiatic, anti-inflammatory and antihypertensive properties. The major component of the extract is the flavonoid 4',5-dihydroxy-6,7-methylenedioxyflavonol-3--α-L-rhamnopyranosyl-(1→2)-β-D-xylopyranoside, but it is not known if this compound is responsible for the biological activity. The objective of this work is to develop effective tools that allow predicting the possible activity of the flavonoid aglycone as an inhibitor of metalloproteases that regulate renal fluid excretion.

Ligand and structure-based discovery of phosphorus-containing compounds as potential metalloproteinase inhibitors.

In this study, a methodology is proposed, combining ligand- and structure-based virtual screening tools, for the identification of phosphorus-containing compounds as inhibitors of zinc metalloproteases. First, we use Dragon molecular descriptors to develop a Linear Discriminant Analysis classification model, which is widely validated according to the OECD principles. This model is simple, robust, stable and has good discriminating power.

A Review of Computational Approaches Targeting SARS-CoV-2 Main Protease to the Discovery of New Potential Antiviral Compounds.

The new pandemic caused by the coronavirus (SARS-CoV-2) has become the biggest challenge that the world is facing today. It has been creating a devastating global crisis, causing countless deaths and great panic. The search for an effective treatment remains a global challenge owing to controversies related to available vaccines.

Ligand-based discovery of new potential acetylcholinesterase inhibitors for Alzheimer's disease treatment.

The enzyme acetylcholinesterase (AChE) is currently a therapeutic target for the treatment of neurodegenerative diseases. These diseases have highly variable causes but irreversible evolutions. Although the treatments are palliative, they help relieve symptoms and allow a better quality of life, so the search for new therapeutic alternatives is the focus of many scientists worldwide.

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

With the advancement of combinatorial chemistry and big data, drug repositioning has boomed. In this sense, machine learning and artificial intelligence techniques offer a priori information to identify the most promising candidates. In this study, we combine QSAR and docking methodologies to identify compounds with potential inhibitory activity of vasoactive metalloproteases for the treatment of cardiovascular diseases.