Predicting Drug Toxicity Using ADMET Models: A Computational Approach for Safer Drug Discovery

Abstract

Drug toxicity is a major challenge in pharmaceutical research and is one of the leading causes of drug failure during preclinical and clinical development. Early prediction of toxic effects is essential for identifying safe and effective drug candidates while reducing the cost and time associated with traditional experimental methods. ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) modeling provides an efficient in silico approach to evaluate the pharmacokinetic and toxicological properties of compounds before laboratory testing. Computational platforms such as Swiss ADME, ProTox3.0, and PubChem enable researchers to predict drug-likeness, oral bioavailability, metabolic interactions, and multiple toxicity endpoints, including hepatotoxicity, mutagenicity, carcinogenicity, and acute oral toxicity, using molecular structure data. These tools support rapid virtual screening, lead optimization, and informed decision-making during drug discovery, thereby minimizing late-stage failures and reducing reliance on animal studies. Although computational predictions require experimental validation, ADMET-based toxicity assessment has become an indispensable component of modern drug development by improving efficiency, safety, and the selection of promising therapeutic candidates.