ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learning

Designing de novo proteins beyond those found in nature holds significant promise for advancements in both scientific and engineering applications. Current methodologies for protein design often rely on AI-based models, such as surrogate models that address end-to-end problems by linking protein structure to material properties or vice versa. However, these models frequently focus on specific material objectives or structural properties, limiting their flexibility when incorporating out-of-domain knowledge into the design process or comprehensive data analysis is required. In this study, …

agent applications beyond collaborations cond-mat.soft cs.ai cs.cl current design designing discovery engineering found language language model large language large language model machine machine learning material multi-agent nature physics protein proteins protein structure q-bio.bm via