Computational approaches for lead compound discovery in dipeptidyl peptidase-4 inhibition using machine learning and molecular dynamics techniques.

The prediction of possible lead compounds from already-known drugs that may present DPP-4 inhibition activity imply a advantage in the drug development in terms of time and cost to find alternative medicines for the treatment of Type 2 Diabetes Mellitus (T2DM). The inhibition of dipeptidyl peptidase-4 (DPP-4) has been one of the most explored strategies to develop potential drugs against this condition. A diverse dataset of molecules with known experimental inhibitory activity against DPP-4 was constructed and used to develop predictive models using different machine-learning algorithms.

Discovery of potent small-molecule inhibitors of lipoprotein(a) formation.

Lipoprotein(a) (Lp(a)), an independent, causal cardiovascular risk factor, is a lipoprotein particle that is formed by the interaction of a low-density lipoprotein (LDL) particle and apolipoprotein(a) (apo(a)). Apo(a) first binds to lysine residues of apolipoprotein B-100 (apoB-100) on LDL through the Kringle IV (K) 7 and 8 domains, before a disulfide bond forms between apo(a) and apoB-100 to create Lp(a) (refs. ).

Anti-glomerular Basement Membrane Glomerulonephritis: A Study in Real Life.

Introduction: Anti-glomerular basement membrane (anti-GBM) disease is a severe entity with few therapeutic options including plasma exchange and immunosuppressive agents. The aim of this study was to analyze the clinical and pathological features that predict the evolution of end-stage kidney disease (ESKD) and the kidney survival in a cohort of patients with anti-GBM disease with renal involvement in real life.

Methods: A retrospective multicentre observational study including 72 patients from 18 nephrology departments with biopsy-proven anti-GBM disease from 1999 to 2019 was performed.

In Silico Searching for Alternative Lead Compounds to Treat Type 2 Diabetes through a QSAR and Molecular Dynamics Study.

Free fatty acid receptor 1 (FFA1) stimulates insulin secretion in pancreatic β-cells. An advantage of therapies that target FFA1 is their reduced risk of hypoglycemia relative to common type 2 diabetes treatments. In this work, quantitative structure-activity relationship (QSAR) approach was used to construct models to identify possible FFA1 agonists by applying four different machine-learning algorithms.