人工智能驱动的药物发现:全面审查
Fábio J N Ferreira1, Agnaldo S Carneiro1
1Universidade Federal do Pará, R. Augusto Corrêa, 01 - Guamá, Belém, Pará 66075-110, Brazil.
ACS omega
|June 23, 2025
概括
人工智能 (AI) 和机器学习 (ML) 正通过提高效率和成功率来彻底改变药物发现. 本综述详细介绍了AI/ML的应用,从目标识别到临床开发,强调了创造更好的药物的挑战和未来方向.
科学领域:
- 制药科学 制药科学
- 计算生物学 计算生物学
- 人工智能的人工智能
背景情况:
- 传统药物发现面临重大障碍,包括高成本,长时间和低成功率.
- 人工智能 (AI) 和机器学习 (ML) 为克服这些挑战提供了有希望的解决方案.
- 最近的进展 (2019-2024) 显示了AI/ML在药物发现管道中的潜力.
研究的目的:
- 在药物发现 (2019-2024) 中批判性地分析最近的AI/ML方法.
- 在关键阶段检查人工智能应用:目标识别,领先发现,优化和安全评估.
- 确定挑战,并提出人工智能在制药研发中的整合未来方向.
主要方法:
- 关于AI/ML在药物发现方面的进展的综合文献综述.
- 分析各种人工智能技术:深度学习,图形神经网络,变压器.
- 对人工智能方法的比较评估,重点关注数据质量,验证和道德.
主要成果:
- 人工智能/ML技术在加速药物发现的各个阶段显示出显著的前景.
- 在目标识别,生成和临床前安全方面确定了关键应用.
- 限制包括数据可访问性,模型可解释性和临床翻译挑战.
结论:
- 人工智能/肌痛性脑脊髓炎的整合为开发更安全,更有效的药物提供了变革性的潜力.
- 解决数据,可解释性和验证方面的挑战对于成功实施至关重要.
- 未来的努力应专注于透明的方法和道德框架,以实现负责任的人工智能采用.
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