药物设计中的人工智能:为什么"适合所有人"的方法仍然无法实现
Rafael Lopes Almeida1,2, Gabriella Matos Campera3, Ina Pöhner2
1Departamento de Engenharia Eletrônica, Escola de Engenharia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
人工智能 (AI) 加快了药物发现,但没有一种AI方法是普遍有效的. 根据特定的研究目标定制人工智能方法是最大限度地发挥其在制药开发中的潜力的关键.
科学领域:
- 制药科学 制药科学
- 计算化学的计算化学
- 生物技术是生物技术.
背景情况:
- 人工智能 (AI) 正在彻底改变药物设计和发现.
- 人工智能提供了降低成本,提高成功率和缩短开发时间的方法.
- 药物开发的复杂性需要不同的AI方法.
研究的目的:
- 在过去五年中审查AI在药物开发中的应用.
- 确定阻碍制药行业广泛采用人工智能的挑战.
- 提供有关当前人工智能的有效性和未来方向的专家意见.
主要方法:
- 关于人工智能工具及其在药物开发中的应用的文献综述.
- 分析挑战,包括数据质量,模型复杂性和计算成本.
- 对AI的作用和局限性的专家意见的综合.
主要成果:
- 在将人工智能应用于各种药物开发目标方面取得了重大进展.
- 关键的挑战包括数据质量,模型复杂性,计算成本和组织障碍.
- 人工智能的有效性高度依赖于上下文,并不普遍适用.
结论:
- 人工智能在药物发现方面的全部潜力目前受到几个因素的限制.
- 一个适合所有人的人工智能解决方案还不能用于药物发现.
- 将人工智能策略与特定的研究目标结合起来,对于成功至关重要.
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