机器智能驱动的虚拟选方法对大数据的进步
Neeraj Kumar1,2, Vishal Acharya1,2
1Artificial Intelligence for Computational Biology Lab (AICoB), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India.
Medicinal research reviews
|December 22, 2023
概括
虚拟选 (VS),使用机器智能,通过有效选大型复合物库来加速药物发现. 这篇评论详细介绍了现代人工智能驱动的VS方法,以实现更快,更准确的打击识别.
科学领域:
- 计算化学和化学信息学
- 药物的发现和开发.
- 医学中的人工智能
背景情况:
- 虚拟查 (VS) 是现代药物发现的基石,传统上分为基于连接体 (LB) 和基于结构 (SB) 的方法.
- 越来越多的化学和生物数据需要先进的计算方法来有效识别被击中分子.
- 机器智能 (MI) 提供了强大的工具来提高VS的速度和准确性,减少时间和资源消耗.
研究的目的:
- 审查和分类各种VS技术,强调机器智能的集成.
- 详细介绍 VS 中机器学习和现代人工智能方法的实施情况.
- 讨论当前VS方法的局限性,并探索未来的前景.
主要方法:
- 关于传统LB和SB VS方法的文献综述.
- 在VS中分析机器学习和AI算法应用.
- 综合LB/SB技术和大数据驱动的VS策略的分类.
主要成果:
- 由MI驱动的VS能够快速选超大库,大大缩短了匹配识别时间.
- 综合LB/SB方法通过考虑连接体和标性质来提高预测准确性.
- 先进的AI方法对于处理大数据和尽量减少药物发现中的错误阳性至关重要.
结论:
- 机器智能正在彻底改变虚拟查,使其成为药物发现中更先进的技术.
- VS的未来在于先进的智能解决方案,能够管理大数据并优化击中/领先的识别.
- 对人工智能和计算架构的持续开发对于克服当前的局限性和提高VS有效性至关重要.
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