DisTrans: Domain Adversarial Training for Cross-Domain Molecular Relational Learning
A novel technique named DisTrans (Domain Adversarial Training Network with Structural-Semantic Transfer Discrepancy) has been introduced for Cross-Domain Molecular Relational Learning (MRL). This method utilizes structure-activity analysis to enhance adaptive representation across different domains for molecular structures and visual imagery. By employing a gradient reversal strategy that targets substructure topological discrepancies, it effectively learns domain dependence. This research tackles the shortcomings of current MRL approaches that prioritize intra-domain modeling, thereby limiting their use in molecular science. The paper can be accessed on arXiv under ID 2605.16799.
Key facts
- DisTrans stands for Domain Adversarial Training Network with Structural-Semantic Transfer Discrepancy.
- The method is designed for Cross-Domain Molecular Relational Learning (MRL).
- It uses structure-activity analysis to optimize cross-domain adaptive representation.
- A gradient reversal strategy based on substructure topological discrepancies is employed.
- The approach addresses the domain-closed effect in existing MRL methods.
- The paper is published on arXiv with ID 2605.16799.
- The method integrates molecular topological and visual modalities.
- The goal is to elucidate cross-domain interaction mechanisms in molecular science.
Entities
Institutions
- arXiv