Scholarlink

<p>Artificial intelligence is increasingly important in drug discovery, particularly in molecular property prediction. Graph Neural Networks can model molecular structures as graphs, using structural data to predict molecular properties and biological activities effectively. However, molecular feature optimization and model integration remain challenges. To address these challenges, we propose MoleculeFormer, a multi-scale feature integration model based on Graph Convolutional Network-Transformer architecture. It uses independent Graph Convolutional Network and Transformer modules to extract features from atom and bond graphs while incorporating rotational equivariance constraints and prior molecular fingerprints. The model captures both local and global features and introduces 3D structural information with invariance to rotation and translation. Experiments on 28 datasets show robust performance across various drug discovery tasks, including efficacy/toxicity prediction, phenotype screening, and ADME evaluation. The integration of attention mechanisms enhances interpretability, and the model demonstrates strong noise resistance, establishing MoleculeFormer as an effective, generalizable solution for molecular prediction tasks.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

MoleculeFormer is a GCN-transformer architecture for molecular property prediction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月25日

查看全文
  • Mingyuan Qin,
  • Ziyan Sun,
  • Lei Feng,
  • Chongyin Han,
  • Jingjing Xia,
  • Lianyi Han

摘要

Artificial intelligence is increasingly important in drug discovery, particularly in molecular property prediction. Graph Neural Networks can model molecular structures as graphs, using structural data to predict molecular properties and biological activities effectively. However, molecular feature optimization and model integration remain challenges. To address these challenges, we propose MoleculeFormer, a multi-scale feature integration model based on Graph Convolutional Network-Transformer architecture. It uses independent Graph Convolutional Network and Transformer modules to extract features from atom and bond graphs while incorporating rotational equivariance constraints and prior molecular fingerprints. The model captures both local and global features and introduces 3D structural information with invariance to rotation and translation. Experiments on 28 datasets show robust performance across various drug discovery tasks, including efficacy/toxicity prediction, phenotype screening, and ADME evaluation. The integration of attention mechanisms enhances interpretability, and the model demonstrates strong noise resistance, establishing MoleculeFormer as an effective, generalizable solution for molecular prediction tasks.