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.

New NADPH Oxidase 2 Inhibitors Display Potent Activity against Oxidative Stress by Targeting p22-p47 Interactions.

NADPH oxidase (NOX2) is responsible for reactive oxygen species (ROS) production in neutrophils and has been recognized as a key mediator in inflammatory and cardiovascular pathologies. Nevertheless, there is a lack of specific NOX2 pharmacological inhibitors. In medicinal chemistry, heterocyclic compounds are essential scaffolds for drug design, and among them, indole is a very versatile pharmacophore.

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.

Chemoinformatics Profiling of the Chromone Nucleus as a MAO-B/A2AAR Dual Binding Scaffold.

Background: In the context of the current drug discovery efforts to find disease modifying therapies for Parkinson's disease (PD) the current single target strategy has proved inefficient. Consequently, the search for multi-potent agents is attracting more and more attention due to the multiple pathogenetic factors implicated in PD. Multiple evidences points to the dual inhibition of the monoamine oxidase B (MAO-B), as well as adenosine A2A receptor (A2AAR) blockade, as a promising approach to prevent the neurodegeneration involved in PD.

Probing the Hypothesis of SAR Continuity Restoration by the Removal of Activity Cliffs Generators in QSAR.

In this work we report the first attempt to study the effect of activity cliffs over the generalization ability of machine learning (ML) based QSAR classifiers, using as study case a previously reported diverse and noisy dataset focused on drug induced liver injury (DILI) and more than 40 ML classification algorithms. Here, the hypothesis of structure-activity relationship (SAR) continuity restoration by activity cliffs removal is tested as a potential solution to overcome such limitation. Previously, a parallelism was established between activity cliffs generators (ACGs) and instances that should be misclassified (ISMs), a related concept from the field of machine learning.