使用基于变压器的方法预测分子描述器属性
1Department of Computer Science, University at Albany, Albany, NY 12203, USA.
International journal of molecular sciences
|August 12, 2023
概括
本研究介绍了半监督机器学习模型,用于使用SMILES字符串预测分子性质. 该方法实现了最先进的性能,即使使用了用于药物发现的基于3D结构的新注意力机制.
科学领域:
- 计算化学是一种计算化学.
- 机器学习在药物发现中的作用
- 化学信息学 化学信息学
背景情况:
- 预测分子性质对于药物发现至关重要.
- 现有的方法通常需要大型标记数据集.
- 机器学习为高效的财产预测提供了潜力.
研究的目的:
- 开发和评估用于预测分子性质的半监督机器学习模型.
- 为了提高模型培训,利用标记和未标记的数据.
- 探索有效的注意力机制用于药物候选人的预测.
主要方法:
- 一个两阶段的方法:在SMILES字符串上使用掩盖语言模型进行预训练,并在下游任务上进行微调.
- 使用标记和未标记的SMILES字符串的大数据集.
- 为端到端的变压器模型开发基于3D结构的新型注意力评分.
主要成果:
- 拟议的半监督模型在MoleculeNet任务上实现了与最先进的方法可比的性能.
- 新的基于3D结构的注意力方法使得预训练模型的性能与预训练模型相比,计算成本降低.
- 该模型成功地预测了抗疟疾药物候选者.
结论:
- 半监督学习有效地预测分子特性,并有助于药物发现.
- 整合3D结构信息提供了计算效率,而不会牺牲性能.
- 开发的模型代表了化学信息学机器学习的重大进步.
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