Grounding Large Language Models (LLMs) in chemical knowledge graphs (KGs) offers a promising way to support synthesis planning, but reliably retrieving information from these complex structures remains a challenge. Therefore, this work addresses that gap by constructing a bipartite KG and evaluating Text2Cypher query generation across both single- and multi-step retrieval tasks. Different prompting strategies were tested, including zero-shot, one-shot with static, random, or embedding-based example selection, and a checklist-driven self-correction pipeline. Results indicate that one-shot prompting is most effective when the exemplar aligns with the query both structurally and logically. When such an exemplar is provided as context to the Cypher generation prompt, self-correction does not yield significant performance gains. Overall, this study introduces a reproducible setup for Text2Cypher experimentation and evaluation.

Accurately predicting enzyme-substrate interactions is critical for applications in drug discovery, biocatalysis and protein engineering. Building upon the ProSmith algorithm, a machine learning framework with a multimodal transformer for protein-small molecule interaction prediction, this study introduces protein 3D structural data as an additional modality. To integrate this data, we explore additive and multiplicative modality fusion strategies without requiring retraining the original transformer from scratch. Our experiments demonstrate that while the incorporation of structural data does not offer improved performance in random splits, it has the potential to surpass ProSmith in challenging data splits involving unseen small molecules. Notably, the model shows better generalization for underrepresented substrates.