In silico approaches for predicting ADME properties of drugs.
In silico approaches for predicting ADME properties of drugs.
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DOI：
10.2133/dmpk.19.327
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发表时间：
2004
影响因子：
2.1
通讯作者：
F. Yamashita;M. Hashida
中科院分区：
文献类型：
--
作者：
F. Yamashita;M. Hashida
组合化学和高通量筛选增加了在比传统药物化学更短的时间内发现新先导化合物的可能性。然而，太多有前途的候选药物往往会因为 ADME 特性不令人满意而失败。计算机 ADME 研究预计将降低药物开发后期损耗的风险，并通过仅关注有前途的化合物来优化筛选和测试。为此，人们开发了许多通过化学结构预测化合物 ADME 性质的计算机方法，从基于数据的方法（例如定量构效关系 (QSAR)、相似性搜索和 3 维 QSAR）到基于结构的方法（例如配体-蛋白质对接和药效团建模）。此外，还研究了几种整合 ADME 特性来预测器官或身体水平药代动力学的方法。在本文中，我们简要总结了计算机 ADME 方法。
Combinatorial chemistry and high-throughput screening have increased the possibility of finding new lead compounds at much shorter time periods than conventional medicinal chemistry. However, too much promising drug candidates often fail because of unsatisfactory ADME properties. In silico ADME studies are expected to reduce the risk of late-stage attrition of drug development and to optimize screening and testing by looking at only the promising compounds. To this end, many in silico approaches for predicting ADME properties of compounds from their chemical structure have been developed, ranging from data-based approaches such as quantitative structure-activity relationship (QSAR), similarity searches, and 3-dimensional QSAR, to structure-based methods such as ligand-protein docking and pharmacophore modelling. In addition, several methods of integrating ADME properties to predict pharmacokinetics at the organ or body level have been studied. In this article, we briefly summarize in silico ADME approaches.