In Silico ADMET Prediction: Recent Advances, Current Challenges and Future Trends
There are numerous small molecular compounds around us to affect our health, such as drugs, pesticides, food
additives, industrial chemicals, and environmental pollutants. Over decades, properties related to absorption, distribution,
metabolism, excretion, and toxicity (ADMET) have become one of the most important issues to assess the effects or risks
of these compounds on human body. Recent high-rate drug withdrawals increase the pressure on regulators and pharmaceutical
industry to improve preclinical safety testing. Since in vivo and in vitro evaluations are costly and laborious, in
silico techniques have been widely used to estimate these properties. In this review, we would briefly describe the recent
advances of in silico ADMET prediction, with emphasis on substructure pattern recognition method that we developed recently.
Challenges and limitations in the area of in silico ADMET prediction were further discussed, such as application
domain of models, models validation techniques, and global versus local models. At last, several new promising research
directions were provided, such as computational systems toxicology (toxicogenomics), data-integration and meta-decision
making systems, which could be used for systemic in silico ADMET prediction in drug discovery and hazard risk assessment.
Keywords: ADMET, drug discovery, hazard risk assessment, systems toxicology, QSAR, data-integration, combined classifier,
substructure pattern recognition.
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