药物发现中的人工智能革命:制药创新的范式转变
Somayah J Jarallah1, Fahad A Almughem1, Nada K Alhumaid1
1Advanced Diagnostics and Therapeutics Institute, Health Sector, King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia.
人工智能 (AI) 通过提高目标识别和设计,降低成本和时间表来加速药物发现. 人工智能整合显示出对开发针对未满足医疗需求的创新疗法的变革性潜力.
科学领域:
- 制药创新 制药创新 制药创新
- 计算生物学 计算生物学
- 药用化学 医学化学
背景情况:
- 传统的药物发现是昂贵的,缓慢的,并且失败率很高.
- 人工智能 (AI) 为这些挑战提供了解决方案.
- 人工智能包括机器学习 (ML),深度学习 (DL) 和自然语言处理 (NLP).
研究的目的:
- 审查人工智能在药物发现中的整合.
- 突出AI对制药开发各个阶段的影响.
- 讨论当前的挑战和人工智能在医学中的未来方向.
主要方法:
- 人工智能算法,包括ML,DL和NLP,在药物开发中应用.
- 像AlphaFold和AtomNet这样的特定AI工具被用于蛋白质结构预测和药物设计.
- 案例研究表明,人工智能在识别候选药物和重新利用现有药物的成功.
主要成果:
- 人工智能显著加速药物发现,提高效率,降低成本.
- 人工智能可以探索广的化学空间,并优化临床试验.
- 成功的AI应用包括识别异常性肺纤维化和COVID-19的治疗方法.
结论:
- 人工智能正在彻底改变制药创新,导致新疗法的开发速度更快.
- 解决数据可访问性和模型可解释性等挑战对于人工智能的持续成功至关重要.
- 人工智能集成为精准医学和为未满足需求提供可访问的治疗铺平了道路.
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