药物发现分子表示的多式预训练模型
Xiaoqi Wang1,2, Chuanshi Wang1,2, Boya Ji3
1School of Computer Science, Northwestern Polytechnical University, Xi'an, 710129, China.
National science review
|January 15, 2026
概括
多模式预训练模型,利用大型语言模型,正在推动药物发现. 将变压器和图形神经网络与分子字幕集成,可以提高准确性和协作.
科学领域:
- 药理学中的人工智能
- 计算机化药物发现技术
- 生物医学信息学 生物医学信息学
背景情况:
- 大型语言模型 (LLM) 对自然语言处理产生了重大影响.
- 自主监督的预训练模型在药物发现中越来越重要.
- 多模式预训练模型为药物发现提供了新的方法.
研究的目的:
- 审查多式联运预培训模型的基础知识.
- 总结它们在药物发现中的进展和应用.
- 确定未来的研究趋势和机会.
主要方法:
- 总结现有的关于药物发现多式联络预培训的文献.
- 分析不同模式和网络框架之间的适应性.
- 突出了变压器和图形神经网络的集成.
- 检查分子标记的作用.
主要成果:
- 多模式预训练模型对药物发现有很大的前景.
- 变压器和图形神经网络使跨度分子表示学习成为可能.
- 分子封面促进了药物发现和LLMs之间的合作.
- 模式的适应性和整合性是关键.
结论:
- 多模式预训练模型是药物发现领域快速发展的领域.
- 未来的研究应该专注于集成先进的架构和有效利用分子数据.
- 应对当前的挑战将释放进一步的潜力,加速药物开发.
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