The field of molecular modeling and prediction is witnessing significant advancements with the integration of graph attention networks, robust fine-tuning methods, and quantum-enhanced multi-task learning. Researchers are focusing on developing models that can capture complex interactions between molecules, proteins, and ligands, and predict their behavior under various conditions. Noteworthy papers in this area include:

• A Graph Attention Network framework that models biological pathways at the gene level, achieving an 81% reduction in MSE when predicting pathway dynamics.
• A robust fine-tuning method, ROFT-MOL, that combines the strengths of simple post-hoc weight interpolation with more complex weight ensemble fine-tuning methods, delivering improved performance across both regression and classification tasks.
• LINKER, a sequence-based model that predicts residue-functional group interactions in terms of biologically defined interaction types, using only protein sequences and the ligand SMILES as input.
• CAMT5, a text-to-molecule model that introduces substructure-level tokenization and an importance-based training strategy, enabling it to better capture molecular semantics.
• QW-MTL, a quantum-enhanced multi-task learning framework that adopts quantum chemical descriptors and a novel exponential task weighting scheme, achieving high predictive performance with minimal model complexity and fast inference.
• CAME-AB, a cross-modality attention framework with a mixture-of-experts backbone for robust antibody binding site prediction, integrating multiple biologically grounded modalities into a unified multimodal representation.