在人工智能时代,基于连接体和结构的虚拟选的联合使用
Jingyi Dai1, Ziyi Zhou1, Yanru Zhao1
1School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, Sichuan, China.
European journal of medicinal chemistry
|December 14, 2024
概括
本综述探讨了机器学习 (ML) 增强的基于连接体 (LBVS) 和基于结构 (SBVS) 虚拟选方法的结合. 它旨在提高药物化学家和人工智能专业人员的药物发现效率.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 医学中的人工智能
背景情况:
- 药物设计寻求时间和成本高效的技术.
- 虚拟选 (基于连接体和基于结构) 可以识别活性化合物,但有局限性.
- 机器学习 (ML) 提供了增强这些方法的机会.
研究的目的:
- 审查ML改进的基于连接体的虚拟查 (LBVS) 和基于结构的虚拟查 (SBVS) 的联合使用.
- 为药物化学家和人工智能专业人员提供有关利用这些综合战略的见解.
- 提高药物发现中的整体查效率.
主要方法:
- 审查关于LBVS和SBVS的现有文献.
- 分析ML应用在增强LBVS和SBVS中的应用.
- 讨论了将ML改进的LBVS和SBVS合并的战略.
主要成果:
- 改进了ML的LBVS和SBVS的协同组合可以克服个体方法的局限性.
- 综合战略为提高命中识别率提供了巨大的潜力.
- 很少有研究全面讨论了这些先进技术的融合.
结论:
- 结合ML增强的LBVS和SBVS是有效药物发现的有希望的策略.
- 本综述强调了综合方法对研究人员的好处和潜力.
- 鼓励在合并这些技术方面进行进一步的研究和开发.
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