Advances in computational modeling are transforming how scientists predict a compound’s Absorption, Distribution, Metabolism, Excretion, and Toxicology (ADMET) profiles. Traditionally, drug candidates were evaluated through laborious in vitro assays and animal studies, processes that are costly, time-intensive, and raise ethical concerns. These conventional methods contribute to the high attrition in drug development, where roughly 90% of candidates fail to reach market, often due to unforeseen pharmacokinetic or toxicity issues discovered late in development. In silico approaches offer a paradigm shift by enabling early-stage predictions of ADME and toxicity directly from molecular structure, thus flagging problematic compounds before substantial resources are invested. This chapter provides a comprehensive overview of the evolution of in silico ADMET prediction methods, from simple rule-based heuristics like Lipinski’s “Rule of Five” to sophisticated machine learning and deep learning models. We categorize these methods—including quantitative structure–activity relationships (QSAR) models, pharmacophore modeling, physiologically based pharmacokinetic (PBPK) simulations, graph neural networks (GNNs), and generative algorithms and critically examine their applications and limitations in predicting key ADME properties (e.g., intestinal absorption, plasma protein binding, blood–brain barrier permeability, metabolic stability, and renal clearance) and various toxicological endpoints (acute and chronic toxicity, organ-specific effects such as hepatotoxicity, cardiotoxicity, and genetic toxicity). We compare popular computational platforms (such as SwissADME, pkCSM, ADMET Predictor, Toxtree, ProTox-II, and VEGA) in terms of methodology, accuracy, and interpretability, highlighting how they complement experimental data. Finally, we discuss future directions that promise to further bridge computational predictions with real-world outcomes, including explainable AI.

By Manish Ramchandani, Neeraj Kumar Kamra, Ashish Kumar Agrahari