在基技术的应用用于药物向发现和药物动力学分析
1Graduate School of Medicine, Kyoto University.
Chemical & pharmaceutical bulletin
|May 31, 2023
概括
在 silico 药物发现中使用人工智能 (AI) 和分子模拟来提高成功率和效率. 这种方法减少了与传统药物开发相关的高成本和长时间.
科学领域:
- 计算化学和生物信息学
- 药物的发现和开发.
- 医学中的人工智能
背景情况:
- 传统的药物发现的特点是成功率低,时间长,成本高.
- 对于提高药物开发的效率和降低药物开发的开支,有着至关重要的需要.
- 信息和通信技术 (ICT) 为这些挑战提供了有希望的解决方案.
研究的目的:
- 为了提供一个关于in silico药物发现方法的概述.
- 突出AI和分子模拟在优化药物开发中的应用.
- 讨论药物标发现和药物动力学分析的计算方法的进展.
主要方法:
- 利用人工智能 (AI) 进行药物标识别和药物动力学分析.
- 采用分子模拟和化学信息学来分析化合物-表型关系.
- 开发用于in silico药物标发现的概率框架.
- 应用非临床数据来预测人类药物动力学参数的疗效和毒性.
主要成果:
- 基于人工智能的方法正在通过阐明化合物-表型路径来改变药物标发现.
- 计算模型有效地从非临床数据中预测人类的药物动力学参数.
- 在 silico 方法简化药物发现过程,解决实验方法的局限性.
结论:
- 利用人工智能和信息和通信技术 (ICT) 进行形药物发现,为克服传统发展障碍提供了一个可行的策略.
- 这些计算方法提高了药物发现中的成功概率和流程效率.
- 生物信息学,系统生物学和化学信息学的整合正在重塑现代药物开发.
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