Abstract

High rates of drug failure cases are a challenge for the pharmaceutical industry to improve preclinical testing. For the ADMET prediction, selection of suitable experimental data and its use in the form of physiological parameters is a challenging task. Nowadays, ADMET prediction is performed at an early stage of drug designing to remove the pharmacokinetic (PK) property of poor compounds. Various ADMET prediction models have been developed using computational algorithms. Experimentally validated ADMET datasets have been analyzed, and related classification features and descriptors were used for the development of in silico models. The current chapter describes the role of ADMET analysis in drug designing, approaches used for model development, existing tools for ADMET prediction, and limitation of predictive models.

By Rajesh Kumar Kesharwani, Virendra Kumar Vishwakarma, Raj K. Keservani, Prabhakar Singh, Nidhi Katiyar, Sandeep Tripathi