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 Multimodal 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. We demonstrate the effectiveness of this framework through large-scale cross-modality retrieval and isomer discrimination tasks. Additionally, ACML enhances interpretability by revealing chemical semantics in graph presentations and bolsters the expressive power of graph neural networks, as evidenced by improved performance in molecular property prediction tasks from MoleculeNet. ACML exhibits its capability to revolutionize chemical research and applications, providing a deeper understanding of the chemical semantics of different modalities.

翻译：多模态深度学习的多功能性为推进科学研究和实际应用带来了巨大前景。随着该领域的持续发展，跨模态分析的集体力量有望推动变革性创新，引领我们走向化学理解与发现的新前沿。为此，我们提出了非对称对比多模态学习（ACML）这一专为分子设计的新方法，展示了其在推动化学领域发展方面的潜力。ACML利用高效的非对称对比学习能力，将来自不同化学模态的信息无缝转移至分子图表示中。通过结合预训练的化学单模态编码器与浅层设计的图编码器，ACML促进了来自不同模态的协调化学语义的融合，从而实现了高效训练下的全面表示学习。我们通过大规模跨模态检索和同分异构体区分任务验证了该框架的有效性。此外，ACML通过揭示图表示中的化学语义增强了可解释性，并提升了图神经网络的表达能力，这在MoleculeNet分子性质预测任务中性能提升的结果中得到了印证。ACML展现了其革新化学研究与应用的潜力，为深入理解不同模态的化学语义提供了有力工具。