药物发现中的计算景观:从AI/ML模型到翻译应用
Deepak Sharma1, Madhu Anabala1, V Vanitha Jain1
1School of Bio-Sciences and Technology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India.
Scientifica
|December 3, 2025
概括
人工智能 (AI) 和机器学习 (ML) 正在通过加速研究和优化药物开发来彻底改变药物发现. 本综述涵盖了AI/ML模型,应用和挑战,突出了强大的药物开发的未来趋势.
科学领域:
- 计算化学是一种计算化学.
- 制药科学 制药科学
- 生物信息学是一种生物信息学.
背景情况:
- 传统的药物发现正在被人工智能 (AI) 和机器学习 (ML) 改变.
- 人工智能/机器学习使得数据驱动的决策,更快的命中识别,以及在药物研究中改进的领先优化成为可能.
- 这些技术是现代药物发现管道的组成部分.
研究的目的:
- 为AI/ML模型及其在药物开发中的应用提供全面的概述.
- 要突出关键的算法,评估指标和AI驱动的工具.
- 讨论AI/ML在药物发现方面的挑战和新兴趋势.
主要方法:
- 审查监督,无监督,半监督,深度学习和强化学习模型.
- 在药物开发阶段对AI/ML应用的分析:目标识别,虚拟查,新分子设计和ADME/T预测.
- 检查广泛使用的算法,性能指标和AI工具.
主要成果:
- 人工智能/ML显著提高了效率,并加速了药物发现的各个阶段.
- 确定的挑战包括数据限制,偏见,可解释性,可复制性和监管障碍.
- 像可解释的人工智能和联合学习这样的新兴趋势为当前的局限性提供了解决方案.
结论:
- 人工智能/ML集成对于现代化药物发现管道至关重要.
- 克服挑战需要计算和实验方法之间的跨学科合作.
- 药物开发中的未来人工智能应用必须强大,道德和临床可转化.
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