IN SILICO ADMET PREDICTIONS: ENHANCING DRUG DEVELOPMENT THROUGH QSAR MODELING
Ms. Suvidhi, Sudesh Kumar, Mr. Sumanshu, Rajesh Kumar Vaid · 2023
In the realm of drug development, accurate prediction of Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties for potential compounds is of paramount importance to identify promising lead candidates at an early stage. In Silico ADMET predictions, driven by the prowess of Quantitative Structure-Activity Relationship (QSAR) models, have emerged as indispensable tools, streamlining the drug discovery pipeline. This article delves into the application of QSAR modeling in ADMET predictions, emphasizing its critical role in prioritizing and fine-tuning lead compounds with enhanced pharmacokinetic and safety profiles. Through the integration of computational techniques, QSAR modeling enables drug developers to make informed decisions, accelerating the identification of safer and more effective therapeutic candidates.