Most drug candidate failures during clinical trials occur due to inappropriate ADMET properties. In this way, there is a major concern to identify possible ADMET failures during the early stages of drug design projects and optimize such properties in order to reduce time and costs. In silico ADMET predictions comprise various strategies that play a central role when considering the task of profiling lead compounds regarding potential ADMET failures. We will discuss the computational strategies, methods and softwares used, actually, to profile ADMET and how they could be helpful during drug design.
Keywords: Absorption, ADME properties, bioavailability, distribution, drug design, excretion, hydrogen bond acceptors, hydrogen bond donors, in silico predictions, Ionization constant, lipophilicity, LogP, metabolism, rule of five, software, solubility, toxicity.