A Unified Multi-modal LLM for Dynamic Protein-Ligand Interactions and Generative Molecular Design
Jing, H.; Miao, Y.
Show abstract
BioDynaGen (Biological Dynamics and Generation) is a novel multi-modal framework unifying protein sequences, dynamic binding site conformations, small molecule ligand SMILES, and natural language text into a single discrete token representation. Built upon a general large language model, BioDynaGen employs continuous pre-training and instruction fine-tuning via next-token prediction to address critical gaps in modeling protein dynamics and ligand interactions. This framework enables a diverse range of tasks, including small molecule-protein binding prediction, dynamic pocket design, and ligand-assisted functional generation. By comprehensively integrating these modalities, BioDynaGen offers an advanced framework for understanding and designing complex biological molecular interactions.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- All-Atom Protein Sequence Design using Discrete Diffusion Models 97%
- Sequence-based Drug-Target Complex Pre-training Enhances Protein-Ligand Binding Process Predictions Tackling Crypticity 96%
- Chemical Genomics Language Model toward Reliable and Explainable Compound-Protein Interaction Exploration 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.