Drug Discovery Insights from a Computational QSAR Study on Alpha-Reductase Inhibitors and Layered Double Hydroxides Electrocatalysts
Year: 2025
Type: article
Abstract: In the ever-evolving landscape of pharmaceuticals, the quest for more effective drug discovery methods has never been more critical. The integration of computational techniques, particularly Quantitative Structure-Activity Relationship (QSAR) studies, has emerged as a transformative approach in identifying promising candidates for therapeutic development. This article delves into the ground-breaking insights gained from a computational QSAR study focusing on alpha-reductase inhibitors class of compounds vital for treating conditions like benign prostatic hyperplasia and androgenic alopecia. Additionally, we will explore the innovative use of layered double hydroxides (LDHs) as electrocatalysts, which can significantly enhance the effectiveness of these inhibitors at the molecular level. By bridging the gap between computational chemistry and practical pharmacology, we can unlock new pathways for drug discovery that promise not only efficiency but also precision in targeting complex biological systems. Join us as we navigate this exciting frontier, where cutting-edge research meets the potential for revolutionary advancements in drug development
Authors: Manoj Kumar, Raghvendra Singh, Alok Singh
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4408886221