ADMET tools in the digital era: Applications and limitations

The high rate of medication failures poses a significant challenge for the pharmaceutical sector. Selecting appropriate data from experiments for ADMET (absorption, distribution, metabolism, excretion, and toxicity) prediction and applying it effectively in the context of physiological characteristics is difficult. Currently, ADMET prediction is conducted early in the drug design process to filter out molecules with weak pharmacokinetic properties. Numerous ADMET models for prediction have been designed using computational methods. Verified ADMET datasets have been determined through experiments, utilizing key classifying factors and descriptors to develop in silico approaches. This chapter discusses the relevance of ADMET evaluation in drug design, methodologies for model creation, available ADMET predictive tools, and the limitations of these predicted models.