Recent Studies of Artificial Intelligence on In Silico Drug Distribution Prediction

Drug distribution is an important process in pharmacokinetics because it has the potential to influence both the amount of medicine reaching the active sites and the effectiveness as well as safety of the drug. The main causes of 90% of drug failures in clinical development are lack of efficacy and uncontrolled toxicity. In recent years, several advances and promising developments in drug distribution property prediction have been achieved, especially in silico, which helped to drastically reduce the time and expense of screening undesired drug candidates. In this study, we provide comprehensive knowledge of drug distribution background, influencing factors, and artificial intelligence-based distribution property prediction models from 2019 to the present. Additionally, we gathered and analyzed public databases and datasets commonly utilized by the scientific community for distribution prediction. The distribution property prediction performance of five large ADMET prediction tools is mentioned as a benchmark for future research. On this basis, we also offer future challenges in drug distribution prediction and research directions. We hope that this review will provide researchers with helpful insight into distribution prediction, thus facilitating the development of innovative approaches for drug discovery.

Automated summary

Drug distribution is a crucial process in pharmacokinetics, as it affects the effectiveness and safety of the drug. Lack of efficacy and uncontrollable toxicity are the major causes of drug failures. Advances in drug distribution property prediction, particularly through in silico methods, have reduced screening time and costs. This study provides comprehensive knowledge on drug distribution, including influencing factors and artificial intelligence-based prediction models. The review also presents future challenges and research directions, aiming to facilitate innovative approaches in drug discovery.