The versatility of multimodal deep learning holds tremendous promise for
advancing scientific research and practical applications. As this field
continues to evolve, the collective power of cross-modal analysis promises to
drive transformative innovations, leading us to new frontiers in chemical
understanding and discovery. Hence, we introduce Asymmetric Contrastive
M}ultimodal Learning (ACML) as a novel approach tailored for molecules,
showcasing its potential to advance the field of chemistry. ACML harnesses the
power of effective asymmetric contrastive learning to seamlessly transfer
information from various chemical modalities to molecular graph
representations. By combining pre-trained chemical unimodal encoders and a
shallow-designed graph encoder, ACML facilitates the assimilation of
coordinated chemical semantics from different modalities, leading to
comprehensive representation learning with efficient training. This innovative
framework enhances the interpretability of learned representations and bolsters
the expressive power of graph neural networks. Through practical tasks such as
isomer discrimination and uncovering crucial chemical properties for drug
discovery, ACML exhibits its capability to revolutionize chemical research and
applications, providing a deeper understanding of chemical semantics of
different modalities.

ACML 是一种新颖的方法，通过有效的不对称对比学习，将来自不同化学模态的信息无缝传递到分子图表示中，进而提供了综合的表示学习和高效训练，增强了学到的表示的可解释性并支持图神经网络的表达能力。通过实际任务，如同分异构体鉴别和揭示药物发现的关键化学性质，ACML 展示了其改革化学研究和应用的能力，并提供对不同模态的化学语义的深入理解。