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

1. 1Train a pre-trained protein language model (PLM) using a large dataset of protein sequences
2. 2Implement Reasoning-Augmented Decoding (RAD) to interleave autoregressive generation with tool calls, such as ESMFold, FoldX, and AutoDock
3. 3Configure the RAD module to consult external biophysical feedback and redirect generation when a candidate violates thermodynamic or structural constraints
4. 4Test the AgentPLM model on a benchmark dataset to evaluate its performance in generating accurate and functional protein sequences
5. 5Apply AgentPLM to real-world protein sequence design tasks, such as designing novel enzymes or protein-protein interactions

Bioinformaticians, computational biologists, and protein engineers can benefit from AgentPLM's capabilities to generate high-quality protein sequences, while machine learning engineers can appreciate the innovative application of reasoning-augmented decoding

💡 AgentPLM's reasoning-augmented decoding enables the model to consult external biophysical feedback and redirect generation, resulting in more accurate and efficient protein sequence design

🧬💻 Introducing AgentPLM: a protein language model with reasoning-augmented decoding for more accurate protein sequence design #proteindesign #AIinbiology