基于图形的变压器来预测八醇-水分区系数
Vyacheslav Grigorev1, Nikita Serov2, Timur Gimadiev1,2
1A.M. Butlerov Institute of Chemistry, Kazan Federal University, 18 Kremlyovskaya Str, Kazan, 420008, Russia.
Journal of cheminformatics
|February 27, 2026
概括
我们开发了GraphormerLogP,这是一种用于预测药物脂友性 (logP) 的深度学习模型. 它在大型数据集上实现了高精度,有助于新药的发现.
科学领域:
- 计算化学和化学信息学
- 药物的发现和开发.
- 制药科学中的机器学习
背景情况:
- 脂性 (logP) 对药物行为至关重要,影响溶解性,透性和新陈代谢.
- 准确的logP预测对于有效的候选药物选择至关重要.
- 基于图形的深度学习模型显示出对分子性质预测的前景.
研究的目的:
- 开发和评估GraphormerLogP,这是一种用于准确logP预测的新型深度学习模型.
- 为培训和评估策划一个庞大,多样化的数据集 (GLP) 超过42,000 SMILES-logP对.
- 将GraphormerLogP的性能与最先进的方法进行比较.
主要方法:
- 使用一个微调的预先训练的GraphormerMapper模型进行logP预测.
- 在新编制的数据集 (GLP) 和基准数据集上训练和测试模型.
- 采用基于图形的深度学习,直接从分子图表学习表示.
主要成果:
- 在两个数据集上,GraphormerLogP实现了竞争性至优异的预测准确性.
- 获得的平均绝对误差 (MAE) 值在GLP数据集上为0.251,在基准数据集上为0.269.
- 与随机森林,Chemprop,CheMeleon,StructGNN和注意力FP相比,证明了该模型的有效性.
结论:
- GraphormerLogP为药物发现中的logP预测提供了一种高性能解决方案.
- 精心策划的GLP数据集为推进脂友性预测研究提供了宝贵的资源.
- 基于图形的深度学习,特别是使用Graphormer,显示出对分子性质预测任务的巨大潜力.
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