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Despite new therapies against malaria, this disease remains one of the main causes of death affecting humanity. The phenomenon of resistance has caused concern as the drugs no longer have the same efficacy, forcing scientific research to develop new methods that prospect new molecules. With the advancement of artificial intelligence, it becomes possible to apply machine learning (ML) techniques in the discovery and evaluation of new molecules, by employing the quantitative structure-activity relationship (QSAR), a classic method that uses regressions to create a model that allows identifying and evaluating new drug candidates. This work combined QSAR with ML and developed a supervised model that modeled a fourth degree polynomial equation capable of identifying new drug candidates derived from the triclosan compound-a classic inhibitor of Plasmodium falciparum growth, the cause of severe malaria. The model produces an R 2 greater than 80% for training and concurrent testing, as well as a correlation index greater than 80% between the calculated and experimental pEC 50 (negative logarithm of half maximal effective concentration) data. In addition, a web software (PlasmoQSAR) was created that allows researchers to calculate the EC 50 (half maximal effective concentration) of new molecules using the developed analytical method., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

Hypotension is one of the main characteristics of the systemic inflammation, basically caused by endothelial dysfunction. Studies have shown that the amino acid L-kynurenine (KYN) causes vasodilation in mammals, leading to hypotensive shock. In hypotensive shock, when activated by the KYN, the voltage-gated potassium channel encoded by the family KCNQ (Kv7) gene can cause vasodilation. Fructose-1,6-bisphosphate (FBP) it is being considered in studies an anti-inflammatory, antioxidant, immunomodulator, and a modulator of some ion channels (Ca2+, Na+, and K+). We analyzed the effects of KYN and FBP on mean blood pressure (MBP), systolic and diastolic (DBP) blood pressure, and heart rate variability (HRV) in Wistar rats. Results demonstrated that the administration of KYN significant decreased MBP, DBP, and increased HRV. Importantly, the FBP treatment reversed the KYN effects on MBP, DBP, and HRV. Molecular Docking Simulations suggested that KYN and FBP present a very close estimated free energy of binding and the same position into structure of KCNQ4. Our results did demonstrate that FBP blunted the decrease in BP, provoked by KYN. Results raise new hypotheses for future and studies in the treatment of hypotension resulting from inflammation., (© 2024 The Author(s). Physiological Reports published by Wiley Periodicals LLC on behalf of The Physiological Society and the American Physiological Society.)

Background: The molecular dynamics is an approach to obtain kinetic and thermodynamic characteristics of biomolecular structures. The molecular dynamics simulation softwares are very useful, however, most of them are used in command line form and continue with the same common implementation difficulties that plague researchers who are not computer specialists., Results: Here, we have developed the VisualDynamics-a WEB tool developed to automate biological simulations performed in Gromacs using a graphical interface to make molecular dynamics simulation user-friendly task. In this new application the researcher can submit a simulation of the protein in the free form or complexed with a ligand. Can also download the graphics analysis and log files at the end of the simulation., Conclusions: VisualDynamics is a tool that will accelerate implementations and learning in the area of molecular dynamics simulation. Freely available at https://visualdynamics.fiocruz.br/login , is supported by all major web browsers. VisualDynamics was developed with Flask, which is a Python-based free and open-source framework for web development. The code is freely available for download at GitHub https://github.com/LABIOQUIM/visualdynamics ., (© 2023. The Author(s).)