AgentPLM: Agentic Protein Language Models with Reasoning-Augmented Decoding for Protein Sequence Design

AgentPLM has been introduced as a novel protein language model that integrates Reasoning-Augmented Decoding (RAD) and Contrastive Agent Policy Optimisation (CAPO) to enhance protein sequence design by allowing real-time consultation of external biophysical feedback. It outperforms existing passive models on benchmark tasks, achieving state-of-the-art results in antibody optimization and other applications, demonstrating improved hit rates and online error correction capabilities. This advancement is significant for practitioners as it enables more adaptive and efficient protein design processes, potentially leading to better therapeutic candidates.