人工智能时代的药物发现:基于目标的变革性方法
Akshata Yashwant Patne1,2, Sai Madhav Dhulipala3, William Lawless3,4
1Center for Research and Education in Nanobioengineering, Department of Internal Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA.
International journal of molecular sciences
|November 27, 2024
概括
机器学习 (ML) 正在通过提高药物标识和设计小分子的准确性和效率来彻底改变药物发现. 诸如简化分子输入线输入系统 (SMILES) 和深度学习等技术加速了领先的识别和虚拟选.
科学领域:
- 计算化学和药理学计算化学和药理学
- 医学中的人工智能
- 药物的发现和开发.
背景情况:
- 药物开发在准确性,速度和效率方面面临挑战,往往限制了成功.
- 机器学习 (ML) 正在越来越多地影响基于目标的药物发现,特别是对于小分子方法.
- 简化分子输入线输入系统 (SMILES) 是在ML应用中表示化学结构的关键工具.
研究的目的:
- 审查最近机器学习发展对基于目标的药物发现的重大影响.
- 要突出ML如何增强药物发现管道的各个阶段,从标识到优化.
- 讨论深度学习和基于碎片的方法在加速药物设计中的作用.
主要方法:
- 使用SMILES的机器学习和自然语言处理用于药物设计,挖掘和重新利用.
- 应用深度学习模型,包括卷积神经网络 (CNN) 和循环神经网络 (RNN),用于虚拟选和目标识别.
- 在基于片段和基于结构的药物设计中使用ML算法和生成对抗网络 (GAN).
主要成果:
- 机器学习模型提高了预测结合亲和力和选择性的准确性,减少了实验查需求.
- 深度学习显示了虚拟查,目标识别和新药设计的前景.
- 在基于碎片和基于结构的方法中,ML加速了击中选择和设计优化.
结论:
- 机器学习正在改变基于目标的药物发现,提高效率和创新.
- 尽管存在诸如可解释性和数据质量等挑战,但机器学习的影响是显著的,并且在不断增长.
- 机器学习在开发各种疾病的新型和改进疗法方面具有巨大的潜力.
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