The field of chemical structure analysis and generation is rapidly advancing, driven by the development of new methods and tools that leverage large language models (LLMs) and machine learning techniques. One of the key directions in this field is the improvement of molecular structure elucidation, which involves deducing a molecule's structure from various types of spectral data. Researchers are also exploring new approaches to molecular generation, including the use of non-equivariant transformer architectures and rotational sampling methods. Additionally, there is a growing interest in the application of LLMs to tasks such as chemical synthesis action extraction and validated molecular design. These advancements have the potential to significantly impact fields such as drug discovery and materials research. Notable papers in this area include Doc2SAR, which proposes a synergistic framework for high-fidelity extraction of structure-activity relationships from scientific documents, and VALID-Mol, which presents a systematic framework for integrating chemical validation with LLM-driven molecular design. Other noteworthy papers include TABASCO, which introduces a fast and simplified model for molecular generation with improved physical quality, and Rotational Sampling, which proposes a novel plug-and-play encoder for rotation-invariant 3D molecular GNNs.