MolGA: Molecular Graph Adaptation with Pre-trained 2D Graph Encoder

• URL: http://arxiv.org/abs/2510.07289v1
• Date: Wed, 08 Oct 2025 17:46:22 GMT
• Title: MolGA: Molecular Graph Adaptation with Pre-trained 2D Graph Encoder
• Authors: Xingtong Yu, Chang Zhou, Xinming Zhang, Yuan Fang,
• Abstract summary: MolGA adapts pre-trained 2D graph encoders to downstream molecular applications by flexibly incorporating diverse molecular domain knowledge.<n>First, we propose a molecular alignment strategy that bridge the gap between pre-trained topological representations with domain-knowledge representations.<n>Second, we introduce a conditional adaptation mechanism that generates instance-specific tokens to enable fine-grained integration of molecular domain knowledge.
• Score: 39.80881797755817
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Molecular graph representation learning is widely used in chemical and biomedical research. While pre-trained 2D graph encoders have demonstrated strong performance, they overlook the rich molecular domain knowledge associated with submolecular instances (atoms and bonds). While molecular pre-training approaches incorporate such knowledge into their pre-training objectives, they typically employ designs tailored to a specific type of knowledge, lacking the flexibility to integrate diverse knowledge present in molecules. Hence, reusing widely available and well-validated pre-trained 2D encoders, while incorporating molecular domain knowledge during downstream adaptation, offers a more practical alternative. In this work, we propose MolGA, which adapts pre-trained 2D graph encoders to downstream molecular applications by flexibly incorporating diverse molecular domain knowledge. First, we propose a molecular alignment strategy that bridge the gap between pre-trained topological representations with domain-knowledge representations. Second, we introduce a conditional adaptation mechanism that generates instance-specific tokens to enable fine-grained integration of molecular domain knowledge for downstream tasks. Finally, we conduct extensive experiments on eleven public datasets, demonstrating the effectiveness of MolGA.

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