文献类型：期刊文献

英文题名：In Silico ADMET Prediction : Recent Advances, Current Challenges and Future Trends

作者：Cheng, Feixiong[1];Li, Weihua[1];Liu, Guixia[1];Tang, Yun[1]

机构：[1]E China Univ Sci & Technol, Sch Pharm, Shanghai Key Lab New Drug Design, Shanghai 200237, Peoples R China

年份：2013

卷号：13

期号：11

起止页码：1273

外文期刊名：CURRENT TOPICS IN MEDICINAL CHEMISTRY

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000320169500003)】；

基金：The authors thank the financial supports from the 863 Project (Grant 2012AA020308), the National Natural Science Foundation of China (Grant 21072059), the Fundamental Research Funds for the Central Universities (WY1113007 and WY1014010), and the Shanghai Committee of Science and Technology (11DZ2260600).

语种：英文

外文关键词：ADMET; drug discovery; hazard risk assessment; systems toxicology; QSAR; data-integration; combined classifier; substructure pattern recognition

摘要：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.