Fig. 1: Overview of KANO.
From: Knowledge graph-enhanced molecular contrastive learning with functional prompt
a, ElementKG construction and embedding. We collect basic element knowledge from the Periodic Table and functional group knowledge from Wikipedia pages to build ElementKG. Then we apply the KG embedding method to obtain the embeddings of all entities and relations in ElementKG. b, Contrastive-based pre-training. We use an element-guided graph augmentation strategy based on element knowledge of ElementKG to convert the original molecular graph G into the augmented molecular graph \(\tilde{G}\), establishing essential connections between atoms beyond the inherent structure. The graph encoders are then trained to maximize the agreement between these two graph views to avoid excessive knowledge injection in \(\tilde{G}\). c, Prompt-enhanced fine-tuning. We leverage functional group knowledge of ElementKG to generate a corresponding functional prompt for each molecule, stimulating the pre-trained graph encoder to recall the learned molecular property-related knowledge and bridging the gap between the pre-training contrastive tasks and the downstream tasks. The resulting prompt-enhanced molecular graph is then fed into the pre-trained graph encoder for molecular property prediction.
