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technologyreview.com·2h ago
AI Research
arXiv CS.AI·Jun 10·5 min read
AgentPLM: Agentic Protein Language Models with Reasoning-Augmented Decoding for Protein Sequence Design
TL;DR
Protein language models (PLMs) traditionally generate sequences without considering external feedback, leading to potential design flaws. AgentPLM enhances a pre-trained PLM by integrating Reasoning-Augmented Decoding (RAD) and Contrastive Agent Policy Optimisation (CAPO) to improve protein sequence design.
✦ Why It Matters
Engineers can leverage AgentPLM to create more effective protein sequences by incorporating real-time feedback into their design processes.
Key Takeaways
- Utilize AgentPLM for improved protein sequence design.
- Incorporate RAD for real-time feedback during generation.
- Leverage CAPO to optimize sequence selection based on feedback.
- Achieve higher success rates in antibody optimization tasks.
How It Works
AgentPLM enhances traditional PLMs by integrating Reasoning-Augmented Decoding (RAD), which allows the model to interleave sequence generation with calls to external tools like ESMFold and FoldX. This enables the model to evaluate the generated sequences against biophysical constraints in real-time.
Additionally, Contrastive Agent Policy Optimisation (CAPO) trains the model to recognize when feedback from these tools is beneficial, allowing it to adjust its generation strategy dynamically.