超大型虚拟选中的目标识别:综合性审查和未来的挑战
Zhe Haw Fang1, Brendan Yong Chern Sim1, Kavinda Kashi Juliyan Gunasinghe2
1Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak Campus, Jalan Simpang Tiga, 93350 Kuching, Sarawak, Malaysia.
Drug discovery today
|January 29, 2026
概括
超大虚拟查 (ULVS) 通过评估数十亿种化合物来加速药物发现. 最近机器学习和多样化工作流程的进步取得了新的成功,尽管在准确性和效率方面存在挑战.
科学领域:
- 计算化学和药物发现
背景情况:
- 超大虚拟选 (ULVS) 对于从庞大的化学库中识别受打击的化合物至关重要.
- 现有的ULVS策略包括基于结构,基于联体,基于药和基于片段的方法.
研究的目的:
- 审查超大虚拟查领域的最新进展和战略.
- 突出机器学习和深度学习在增强ULVS中的作用.
- 讨论成功的平台及其在各种目标类别的命中发现中的应用.
主要方法:
- 对ULVS工作流程的最新文献的调查.
- 分析机器学习和深度学习在选中的整合.
- 像VirtualFlow,RosettaVS,深度对接和V-SYNTHES这样的平台的案例研究.
主要成果:
- ULVS平台已经成功地确定了新的,验证的对GPCR和蛋白质-蛋白质接口等目标的成功.
- 机器学习和深度学习显著增强了传统的ULVS方法.
- 越来越多地采用各种ULVS策略,包括基于片段和混合方法.
结论:
- 尽管取得了成功,但提高得分准确度,资源效率和通用性仍然是ULVS的关键挑战.
- 未来的ULVS将专注于ML引导的自适应探索和合成意识的设计.
- 在ULVS中持续创新对于药物开发中高效的成功发现至关重要.
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