AISMPred: A Machine Learning Approach for Predicting Anti-Inflammatory Small Molecules.

Inflammation serves as a vital response to diverse harmful stimuli like infections, toxins, or tissue injuries, aiding in the elimination of pathogens and tissue repair. However, persistent inflammation can lead to chronic diseases. Peptide therapeutics have gained attention for their specificity in targeting cells, yet their development remains costly and time-consuming.

Targeting Yes-Associated Protein (YAP) in Breast Cancer: In Silico Molecular Dynamics, Luminescence-Based In Vitro, and In Vivo Validation of Rauvolfia tetraphylla-Derived Inhibitors.

The study aims to elucidate the pharmacological mechanism of Rauvolfia tetraphylla against breast cancer through a comprehensive, multi-faceted approach. This includes molecular docking, molecular dynamics, and experimental validation. Initial screening via ADME analysis and network pharmacology identified key compounds and potential targets.

In-silico binding affinity of a phage display library screened novel peptide against various FABPs.

In accordance to the American Heart Association (AHA), cardiovascular diseases (CVDs) are the leading cause of death around the globe, causing more than 19.1 million deaths in 2020. Heart-type fatty acid binding protein (H-FABP) is required for the metabolism of fatty acids (FA) inside cardiomyocytes is reported as a biomarker for myocardial damage.