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RetroGEF, a flow-based generative model for single-step retrosynthesis that learns this process directly from product--reactant pairs without requiring a prescribed edit order, achieves state-of-the-art performance.
Examining task affinities from supervised fine-tuning gradients and solving a constrained mixed-integer program(MIP) to construct partially overlapping specialist groups highlights specialization and capability integration as connected but distinct design problems in chemical reasoning.
Experiments show that BoMolLLM improves over one-shot prompting, is competitive with or stronger than GP-based BO baselines, and reveals a domain-dependent interface: reference-only transfer works best for binary drug targets, while adding a concise surrogate summary is more beneficial for continuous material.
This survey provides a first systematic overview of the emerging area of vision meets graphs, which treats visual depictions of graphs as first-class inputs for reasoning and learning, and organize existing work into three threads.
This work introduces \textbf{MolEmb}, a lightweight framework that adapts MLLMs by aligning molecular profiles with textual descriptions in a shared embedding space using a bidirectional contrastive objective, and finds that context-aware molecular embedding is primarily a data property of the supervision.
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