提高候选药物的ADMET预测精度:在QSPR建模方法中需要考虑的因素
Meilun Chen1, Jie Yang1, Chunhua Tang1
1Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Changsha, Hunan, 410013, China.
定量结构与属性关系 (QSPR) 模型预测药物特性,但需要改进. 本综述涵盖了用于预测药物吸收,分布,新陈代谢,分泌和毒性 (ADMET) 的QSPR工具和方法,以提高准确性.
科学领域:
- 计算化学和化学信息学
- 药理学和药物发现
- 毒理学和安全性评估.
背景情况:
- 定量结构与属性关系 (QSPR) 模型将分子结构/属性与药物动力学行为联系起来.
- QSPR对于预测药物吸收,分布,新陈代谢,分泌和毒性 (ADMET) 是至关重要的.
- 目前的QSPR模型需要提高可靠药物开发的预测准确性.
研究的目的:
- 对药物ADMET预测的QSPR工具和方法进行全面审查.
- 分析各种QSPR建模方法的优点和局限性.
- 确定挑战,并提出改善药物开发中的QSPR模型准确性的策略.
主要方法:
- 对QSPR建模技术进行系统审查.
- 分析QSPR预测不同阶段使用的工具.
- 评估QSPR应用中对ADMET属性的最新进展.
主要成果:
- 通常使用的QSPR模型构建方法的总结.
- 识别不同建模方法的优缺点.
- 基于QSPR的ADMET预测的近期进展概述.
结论:
- QSPR是预测药物ADMET特性的一个有价值的工具.
- 解决固有的挑战和实施建议的考虑因素可以提高QSPR的准确性.
- 本综述为优化药物发现和开发中的QSPR模型提供了指导.
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