SimSon: SMILES的简单对比学习用于分子性质预测
Chae Eun Lee1, Jin Sob Kim1, Jin Hong Min1
1Department of Industrial and Management Engineering, Korea University, Seoul, 02841, Republic of Korea.
Bioinformatics (Oxford, England)
|May 9, 2025
概括
一个新的自我监督框架,简单的微笑对比学习 (SimSon),增强了分子性质的预测. 通过对未标记的数据进行预训练,SimSon提高了药物发现应用的模型概括性和稳定性.
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
背景情况:
- 深度学习加速了药物发现,但面临着有限的标记数据和跨化学空间的概括性挑战.
- 准确的分子性质预测对于有效的药物发现和逆合成至关重要.
研究的目的:
- 提出一个自我监督的框架,简单的SMILES对比学习 (SimSon),以获得强大的简化分子输入线输入系统 (SMILES) 表示.
- 在未标记的SMILES数据上利用对比学习来提高模型概括性和分子性质预测中的稳定性.
主要方法:
- 开发了SimSon,这是一个自我监督的框架,利用对比学习在未标记的SMILES数据上.
- 预训练的SimSon可以捕捉SMILES表示中的语义上下文.
- 与现有的基于图表的方法相比,评估了SimSon在下游任务中的表现.
主要成果:
- 使用随机 SMILES 的对比学习通过捕捉全球分子语义上下文来增强模型的概括性和稳定性.
- 在下游任务中,SimSon表现出与基于图形的方法相比具有竞争力的性能,在特定的基准数据集上表现优于它们.
- 该框架有效地捕捉了SMILES的结构信息,显示出显著的概括性和稳定性.
结论:
- SimSon为分子性质预测提供了强大的方法,通过自我监督学习克服了数据限制.
- 该方法对生物信息学和化学信息学的应用有希望,包括药物发现和药物相互作用预测.
- 对于SimSon的源代码是公开可用的,用于进一步的研究和开发.
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