人工智能革命药物开发:探索机遇和挑战
Prafulla C Tiwari1, Rishi Pal1, Manju J Chaudhary2
1Department of Pharmacology and Therapeutics, King George's Medical University, Lucknow, Uttar Pradesh, India.
Drug development research
|September 15, 2023
概括
人工智能 (AI) 通过快速选化合物和预测疗效,加速药物发现. 挑战包括数据质量,模型可解释性和AI在医学中的伦理考虑.
科学领域:
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
- 生物技术是生物技术.
背景情况:
- 传统的药物发现是漫长的,昂贵的,并且失败率很高.
- 现有的方法与生物系统的复杂性和广的化学空间作斗争.
- 需要创新的方法来提高治疗开发的效率和成功率.
研究的目的:
- 审查人工智能 (AI) 和机器学习 (ML) 在药物发现中的变革潜力.
- 突出AI在加速化合物查和预测药物特性方面的能力.
- 检查与在药物开发管道中实施AI相关的挑战.
主要方法:
- 关于AI在药物发现中的应用现有文献的综述.
- 分析AI在复合图书馆选和预测建模中的作用.
- 讨论数据要求,模型可解释性和人工智能部署的伦理考虑.
主要成果:
- 人工智能显著提高了识别潜在药物候选者的速度和效率.
- 人工智能算法可以预测化合物的有效性和安全性,减少对广泛测试的需求.
- 人工智能集成有望简化药物开发过程,提高临床试验成功率.
结论:
- 人工智能提供了一个强大的工具包,可以克服传统药物开发的局限性.
- 解决数据采集,可解释性和伦理方面的挑战对于成功采用人工智能至关重要.
- 人工智能有可能开启一个更有效,更安全的治疗时代.
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