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The versatility of multimodal deep learning holds tremendous promise foradvancing scientific research and practical applications. As this fieldcontinues to evolve, the collective power of cross-modal analysis promises todrive transformative innovations, leading us to new frontiers in chemicalunderstanding and discovery. Hence, we introduce Asymmetric ContrastiveMultimodal Learning (ACML) as a novel approach tailored for molecules,showcasing its potential to advance the field of chemistry. ACML harnesses thepower of effective asymmetric contrastive learning to seamlessly transferinformation from various chemical modalities to molecular graphrepresentations. By combining pre-trained chemical unimodal encoders and ashallow-designed graph encoder, ACML facilitates the assimilation ofcoordinated chemical semantics from different modalities, leading tocomprehensive representation learning with efficient training. This innovativeframework enhances the interpretability of learned representations and bolstersthe expressive power of graph neural networks. Through practical tasks such asisomer discrimination and uncovering crucial chemical properties for drugdiscovery, ACML exhibits its capability to revolutionize chemical research andapplications, providing a deeper understanding of chemical semantics ofdifferent modalities.

Asymmetric Contrastive Multimodal Learning for Advancing Chemical Understanding