Application of in Silico Technologies for Drug Target Discovery and Pharmacokinetic Analysis

Drug discovery is researched and developed through many processes, but its overall success rate is extremely low, requiring a very long period of development and considerable costs. Clearly, there is a need to reduce research and development costs by improving the probability of success and increasing process efficiency. One promising approach to this challenge is so-called "in silico drug discovery," which is drug discovery utilizing information and communications technologies (ICT) such as artificial intelligence (AI) and molecular simulation. In recent years, ICT-based science and technology, such as bioinformatics, systems biology, cheminformatics, and molecular simulation, which have been developed mainly in the life science and chemistry fields, have changed the face of drug development. AI-based methods have been developed in the drug discovery process, mainly in relation to drug target discovery and pharmacokinetic analysis. In drug target discovery, an in silico method has been developed that uses a probabilistic framework that eliminates the problems of conventional experimental approaches and provides a key to understanding the pathways and mechanisms from compounds to phenotypes. In the field of pharmacokinetic analysis, we have seen the development of a method using nonclinical data to predict human pharmacokinetic parameters, which are important for predicting drug efficacy and toxicity in clinical trials. In this article, we provide an overview of these methods.