人工智能驱动的药物向相互作用预测和优化的进展
Qiqi Wang1,2, Boyan Sun1,2, Yunpeng Yi3
1State Key Laboratory of Veterinary Public Health and Safety, College of Veterinary Medicine, China Agricultural University, Beijing 100193, China.
人工智能 (AI) 通过分析分子结构和预测药物向相互作用来加速药物发现. 这篇评论强调了人工智能.
科学领域:
- 制药研发领域的研发工作.
- 计算化学计算化学
- 生物信息学是一种生物信息学.
背景情况:
- 药物发现是制药研发中的一个复杂而昂贵的过程.
- 人工智能 (AI) 提供了强大的工具来增强药物发现和开发的各个阶段.
- 人工智能促进了分子特征提取,药物向相互作用分析和疾病关系建模.
研究的目的:
- 审查药物设计人工智能应用的最新进展.
- 提供对人工智能驱动的目标识别,合成可访问性,优化和ADMET属性评估策略的见解.
- 为在制药研究中实施AI建立一个概念框架.
主要方法:
- 关于AI在药物发现中的应用现有文献的综述.
- 深度学习工具和方法的总结.
- 对不同药物发现阶段的AI驱动策略的组织.
主要成果:
- 人工智能提高了预测准确度,加快了时间表,并降低了药物发现的成本.
- 人工智能应用包括目标识别,合成可访问性,优化和ADMET评估.
- 深度学习工具对于引导人工智能实施越来越重要.
结论:
- 人工智能显著提高了制药研发的效率和成功率.
- 对人工智能实施的结构化方法对于推动药物发现至关重要.
- 本综述为在制药研究中利用人工智能方法提供了一个框架.
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