自然产品药物发现的人工智能
Michael W Mullowney1, Katherine R Duncan2, Somayah S Elsayed3
1Duchossois Family Institute, The University of Chicago, Chicago, IL, USA.
Nature reviews. Drug discovery
|September 11, 2023
概括
计算奥米克和人工智能加速了天然产品药物发现. 这些领域之间的协同作用可以识别新药候选者,克服数据质量和算法验证方面的挑战.
科学领域:
- 计算生物学和生物信息学
- 药物的发现和开发.
- 自然产品化学 自然产品化学
背景情况:
- 计算奥米克技术使得用于药物发现的自然产品多样性的探索成为可能.
- 人工智能 (AI),包括机器学习,推进了计算药物设计,帮助活动预测和de novo设计.
- 奥米克和人工智能之间的协同作用为从自然来源识别新药候选人提供了强大的工具.
研究的目的:
- 描述计算奥米克和人工智能之间的当前和未来协同作用,用于天然产品药物发现.
- 突出整合这些技术用于识别候选药物的潜力.
- 讨论这些综合方法的挑战和战略,以实现这些综合方法的全部潜力.
主要方法:
- 利用计算奥米克用于自然产品数据生成和分析.
- 应用机器学习和人工智能算法用于生物活动预测.
- 利用新的药物设计方法来优化分子标.
- 为人工智能模型培训制定高质量的数据集策划策略.
主要成果:
- 从自然产品库中识别新药候选药物.
- 增强对自然化合物的生物活动的预测.
- 促进潜在治疗药物的新设计.
- 讨论在天然产品药物发现中成功实施AI的关键因素.
结论:
- 计算奥米克斯和人工智能的整合为天然产品药物发现提供了一个变革性的方法.
- 解决与数据质量和算法验证相关的挑战对于最大限度地发挥这些协同技术的影响至关重要.
- 未来的研究应该专注于强大的验证和高质量的数据集的开发,以充分利用AI在这个领域的潜力.
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