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.

Exploring blood-brain barrier passage using atomic weighted vector and machine learning.

Context: This study investigates the potential of leveraging molecular properties, as determined by MD-LOVIs software and machine learning techniques, to predict the ability of compounds to cross the blood-brain barrier (BBB). Accurate prediction of BBB permeation is critical for the development of central nervous system (CNS) drugs. The study applies various machine learning models, including both classification and regression techniques, to predict BBB passage and molecular activity.

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.

Anti-inflammatory Activity: and of Sapogenins Present in subsp. (Trel.).

Background: Agave brittoniana subsp. brachypus is an endemic plant of Cuba, which contains different steroidal sapogenins with anti-inflammatory effects. This work aims to develop computational models which allow the identification of new chemical compounds with potential anti-inflammatory activity.

Exploring proteasome inhibition using atomic weighted vector indices and machine learning approaches.

Ubiquitin-proteasome system (UPS) is a highly regulated mechanism of intracellular protein degradation and turnover. The UPS is involved in different biological activities, such as the regulation of gene transcription and cell cycle. Several researchers have applied cheminformatics and artificial intelligence methods to study the inhibition of proteasomes, including the prediction of UPP inhibitors.