The recent advancements in the field of molecular and biomedical research are marked by a significant shift towards leveraging large language models (LLMs) and graph-based representations to enhance predictive capabilities and domain adaptation. A notable trend is the integration of LLMs with molecular data, which is being explored to improve tasks such as molecular property prediction, drug-drug interaction prediction, and molecular design. This integration is facilitated by novel techniques such as Schema Augmentation and hierarchical transformer architectures, which aim to bridge the gap between textual and molecular data, thereby enhancing the model's ability to generalize across different domains. Additionally, the use of graph-based methods, such as Graph Fourier Neural ODEs, is being extended to model complex molecular dynamics, offering a more comprehensive approach to understanding and predicting molecular behavior. Furthermore, the field is witnessing a move towards self-supervised learning and hierarchical representation for tasks like medication recommendation, which promises to improve the accuracy and interpretability of predictive models in healthcare. Notably, the development of specialized LLMs for chemistry, such as LLaMo, is demonstrating superior performance in generating molecular descriptions and predicting properties, underscoring the potential of domain-specific pre-training. These innovations collectively push the boundaries of what is possible in molecular and biomedical research, offering new avenues for more accurate and efficient drug discovery and healthcare solutions.