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

• URL: http://arxiv.org/abs/2606.02386v2
• Date: Tue, 09 Jun 2026 09:11:47 GMT
• Title: AgentPLM: Agentic Protein Language Models with Reasoning-Augmented Decoding for Protein Sequence Design
• Abstract summary: Protein language models (PLMs) generate sequences in a single forward pass with no mechanism to consult external biophysical feedback or redirect generation.<n>We introduce AgentPLM, which addresses this by equipping a pre-trained PLM with i) Reasoning-Augmented Decoding (RAD), which interleaves autoregressive generation with tool calls.<n>We evaluate AgentPLM on benchmark tasks spanning de novo enzyme design, antibody optimisation, thermostability, PPI interface design, and zero-shot fitness prediction with standardised oracle APIs.
• Score: 0.48342038441006796
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Protein language models (PLMs) are passive oracles: they generate sequences in a single forward pass with no mechanism to consult external biophysical feedback or redirect generation when a candidate violates thermodynamic or structural constraints. We introduce AgentPLM, which addresses this by equipping a pre-trained PLM with i) Reasoning-Augmented Decoding (RAD), which interleaves autoregressive generation with tool calls (ESMFold, FoldX, AutoDock Vina), and ii) Contrastive Agent Policy Optimisation (CAPO), a trajectory-level extension of direct preference optimisation that trains the policy end-to-end to learn when oracle feedback is informative rather than merely imitating high-fitness sequences. We evaluate AgentPLM on benchmark tasks spanning de novo enzyme design, antibody optimisation, thermostability, PPI interface design, and zero-shot fitness prediction with standardised oracle APIs and controlled sequence-identity splits. AgentPLM achieves state-of-the-art results with a gain in antibody top-10% hit rate over the strongest passive baseline, providing mechanistic evidence of online error correction without explicit backtracking.

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