Synergistic Fusion of Graph and Transformer Features for Enhanced Molecular Property Prediction
Mukkamala Venkata Sai Prakash, Siddartha Reddy, Ganesh Parab, V Varun, Vishal Vaddina, Saisubramaniam Gopalakrishnan · 2023
Molecular property prediction is a critical task in computational drug discovery. While recent advances in Graph Neural Networks (GNNs) and Transformers have shown to be effective and promising, they face the following limitations: Transformer self-attention does not explicitly consider the underlying molecule structure while GNN feature representation alone is not sufficient to capture granular and hidden interactions and characteristics that distinguish similar molecules. To address these limitations, we explore synergistically combining pre-trained features from GNNs and Transformers and term it as SYN-FUSION. This comprehensive molecular representation captures both the global molecule structure and individual atom characteristics.