In recent years, the growth of in silico studies to evaluate pharmacokinetic and toxicological properties has taken on a relevant role in the drug discovery and development process in the pharmaceutical industry. The incorporation of methodologies based on AI in the generation of in silico predictive models, for the determination of ADMET properties in real compound libraries, virtual chemical library, chemical libraries databases, and combinatorial chemistry libraries, has allowed the development of software standalone and online web platforms that promote the study of ADMET in silico . The existence of numerous computational tools to predict ADMET properties in chemical compounds constitutes one of the main limitations of these in silico methods, as pointed out by Kar, S., and Leszczynski, J. , in their extensive work on in silico tools to predict the ADMET profile.