Deep Interactions for Multimodal Molecular Property Prediction

Multi-modal learning by means of leveraging both 2D graph and 3D point cloud information has become a prevalent method to improve model performance in molecular property prediction. However, many recent techniques focus on specific pre-training tasks such as contrastive learning, feature blending, and atom/subgraph masking in order to learn multi-modality even though design of model architecture is also impactful for both pre-training and downstream task performance. Relying on pre-training tasks to align 2D and 3D modalities lacks direct interaction which may be more effective in multimodal learning. In this work, we propose MolInteract, which takes a simple yet effective architecture-focused approach to multimodal molecule learning which addresses these challenges. MolInteract leverages an interaction layer for fusing 2D and 3D information and fostering cross-modal alignment, showing strong results using even the simplest pre-training methods such as predicting features of the 3D point cloud and 2D graph. MolInteract exceeds several current state-of-the-art multimodal pre-training techniques and architectures on various downstream 2D and 3D molecule property prediction benchmark tasks.