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Enhancing accurate molecular property predic- tion relies on effective and proficient representa- tion learning. It is crucial to incorporate diverse molecular relationships characterized by multi- similarity (self-similarity and relative similarities) (Wang et al., 2019) between molecules. However, current molecular representation learning meth- ods fall short in exploring multi-similarity and of- ten underestimate the complexity of relationships between molecules. Additionally, previous multi- similarity approaches require the specification of positive and negative pairs to attribute distinct pre- defined weights to different relative similarities, which can introduce potential bias. In this work, we introduce Graph Multi-Similarity Learning for Molecular Property Prediction (GraphMSL) framework, along with a novel approach to for- mulate a generalized multi-similarity metric with- out the need to define positive and negative pairs. In each of the chemical modality spaces (e.g., molecular depiction image, fingerprint, NMR, and SMILES) under consideration, we first de- fine a self-similarity metric (i.e., similarity be- tween an anchor molecule and another molecule), and then transform it into a generalized multi- similarity metric for the anchor through a pair weighting function. GraphMSL validates the effi- cacy of the multi-similarity metric across Molecu- leNet datasets. Furthermore, these metrics of all modalities are integrated into a multimodal multi-similarity metric, which showcases the po- tential to improve the performance. Moreover, the focus of the model can be redirected or cus- tomized by altering the fusion function. Last but not least, GraphMSL proves effective in drug dis- covery evaluations through post-hoc analyses of the learnt representations. 

Nuclear magnetic resonance (NMR) spectroscopy plays an essential role in deciphering molecular structure and dynamic behaviors. While AI-enhanced NMR prediction models hold promise, challenges still persist in tasks such as molecular retrieval, iso- mer recognition, and peak assignment. In response, this paper introduces a novel solution, Knowledge-Guided Multi-Level Multimodal Alignment with Instance-Wise Discrimination (K-M3 AID), which establishes correspondences between two heterogeneous modalities: molecular graphs and NMR spectra. K- M3AID employs a dual-coordinated contrastive learning architecture with three key modules: a graph-level alignment module, a node-level alignment module, and a communication channel. Notably, K-M3AID introduces knowledge- guided instance-wise discrimination into contrastive learning within the node-level alignment module. In addition, K-M3 AID demonstrates that skills acquired during node-level alignment have a positive impact on graph-level alignment, acknowledging meta-learning as an inherent property. Empirical validation underscores the effectiveness of K-M3AID in multiple zero- shot tasks.