AdaptMol: Adaptive Fusion from Sequence String to Topological Structure for Few-shot Drug Discovery
This work addresses the challenge of data scarcity in AI-driven drug discovery, offering an incremental improvement for researchers in computational chemistry.
The paper tackled the problem of accurate molecular property prediction under few-shot learning scenarios by proposing AdaptMol, a prototypical network that adaptively fuses SMILES sequences and molecular graphs, achieving state-of-the-art performance on benchmarks in 5-shot and 10-shot settings.
Accurate molecular property prediction (MPP) is a critical step in modern drug development. However, the scarcity of experimental validation data poses a significant challenge to AI-driven research paradigms. Under few-shot learning scenarios, the quality of molecular representations directly dictates the theoretical upper limit of model performance. We present AdaptMol, a prototypical network integrating Adaptive multimodal fusion for Molecular representation. This framework employs a dual-level attention mechanism to dynamically integrate global and local molecular features derived from two modalities: SMILES sequences and molecular graphs. (1) At the local level, structural features such as atomic interactions and substructures are extracted from molecular graphs, emphasizing fine-grained topological information; (2) At the global level, the SMILES sequence provides a holistic representation of the molecule. To validate the necessity of multimodal adaptive fusion, we propose an interpretable approach based on identifying molecular active substructures to demonstrate that multimodal adaptive fusion can efficiently represent molecules. Extensive experiments on three commonly used benchmarks under 5-shot and 10-shot settings demonstrate that AdaptMol achieves state-of-the-art performance in most cases. The rationale-extracted method guides the fusion of two modalities and highlights the importance of both modalities.