Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.
Curr Top Med Chem. 2013;13(11):1273-89. doi: 10.2174/15680266113139990033.
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.
我们周围有许多小分子化合物会影响我们的健康，如药物、农药、食品添加剂、工业化学品和环境污染物。几十年来，与吸收、分布、代谢、排泄和毒性（ADMET）相关的性质已成为评估这些化合物对人体影响或风险的最重要问题之一。最近大量药物撤回增加了监管机构和制药行业提高临床前安全测试的压力。由于体内和体外评估既昂贵又费力，因此已广泛使用计算技术来估算这些性质。在这篇综述中，我们将简要描述近年来计算 ADMET 预测的最新进展，重点介绍我们最近开发的基于子结构模式识别方法。进一步讨论了计算 ADMET 预测领域的挑战和局限性，例如模型的应用领域、模型验证技术以及全局与局部模型。最后，提供了几个新的有前途的研究方向，例如计算系统毒理学（毒代动力学）、数据集成和元决策系统，可用于药物发现和危害风险评估中的系统计算 ADMET 预测。
