通过以任务为导向的转移学习增强分子性质预测:整合通用结构洞察力和特定领域的知识
Yanjing Duan1, Xixi Yang2, Xiangxiang Zeng2
1Xiangya School of Pharmaceutical Sciences, Central South University, Changsha Hunan 410013, P. R. China.
Journal of medicinal chemistry
|May 15, 2024
概括
我们基于BERT (TOML-BERT) 开发了以任务为导向的多级学习,以改善药物发现中的分子性质预测. 这种方法通过整合结构模式和领域知识来增强深度学习,实现最先进的结果.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 机器学习 机器学习
背景情况:
- 准确的分子性质预测对于药物发现至关重要.
- 深度学习方法受到有限的标记数据的挑战.
- 现有的自我监督预培训往往忽视了关键的领域特定知识.
研究的目的:
- 引入一种新的双层预培训框架,即基于BERT (TOML-BERT) 的面向任务的多层次学习.
- 通过结合分子结构和领域知识来解决当前预训练方法的局限性.
- 提高深度学习模型在分子性质预测中的性能.
主要方法:
- 开发了TOML-BERT,这是一个使用BERT架构的双层预培训框架.
- 将分子结构模式和特定领域的知识整合到预训练过程中.
- 采用大量的伪标签数据来提取知识,并在分子结构内挖掘情境信息.
主要成果:
- 在10个不同的制药数据集中实现了最先进的预测性能.
- 通过双级预训练组件的互补贡献,证明了显著的积极转移.
- 展示了挖掘上下文信息和有效提取领域知识的能力.
结论:
- 在药物发现中,TOML-BERT显著推进了分子性质预测.
- 双层预训练有效地学习与任务相关的分子表示.
- 结合多个预训练任务具有很大的潜力,可以提取以任务为导向的知识.
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