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.
