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数字时代的ADMET工具:应用和局限性
Sonali S Shinde1, Prabhanjan S Giram2, Pravin S Wakte1
1Department of Chemical Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India.
Advances in pharmacology (San Diego, Calif.)
|April 2, 2025
概括
药物开发面临着药物失败带来的挑战. 本研究审查了吸收,分布,新陈代谢,分泌和毒性 (ADMET) 预测方法,以改善早期药物设计并减少失败.
科学领域:
- 药理学和制药科学 药理学和制药科学
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 药物失效率高,是制药开发中的一个重大障碍.
- 准确预测吸收,分布,新陈代谢,分泌和毒性 (ADMET) 特性对于成功的药物设计至关重要.
- 当前的挑战包括选择相关的实验数据,并将生理特征整合到ADMET预测中.
研究的目的:
- 讨论ADMET评估在药物设计过程中的重要性.
- 审查创建计算ADMET预测模型的方法.
- 探索可用的in silico ADMET预测工具及其局限性.
主要方法:
- 使用经过验证的实验ADMET数据集.
- 使用关键分类因子和分子描述符.
- 开发用于ADMET预测的in silico方法.
- 审查计算方法和现有的预测工具.
主要成果:
- 计算方法导致了许多ADMET预测模型.
- 在 silico 方法是使用实验数据和关键因素开发的.
- 在药物设计的早期,ADMET预测对于过具有不良药理学特性的分子至关重要.
结论:
- 对于高效的药物设计,ADMET评估非常重要.
- 为了预测ADMET存在各种计算方法和工具.
- 了解预测模型的局限性对于其有效应用至关重要.
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