AgentPLM

Agentic protein language model that interleaves autoregressive sequence generation with tool calls (ESMFold, FoldX, AutoDock Vina) under Reasoning-Augmented Decoding, trained end-to-end via Contrastive Agent Policy Optimisation to learn when oracle feedback is informative.

   
Affiliation Bedford College, London (Sahil Rahman) and Saarland University (Maxx Richard Rahman)
First introduced 2026-06 (arXiv preprint); accepted to ICML 2026
Lifecycle stages Experiment design (the agent decides which biophysical oracle to query during in-silico protein-sequence design)
Autonomy level Semi-autonomous (policy-driven tool selection during decoding; no wet-lab loop)
Domain focus Biology / computational protein engineering
Availability Closed (no code release noted in the preprint)

Approach

Each design step is modelled as a decision in a Partially Observable Markov Decision Process over the joint space of partial sequences and retrieved biophysical context. Two contributions:

  • Reasoning-Augmented Decoding (RAD) — interleaves autoregressive PLM generation with structured tool calls to ESMFold, FoldX, and AutoDock Vina, incorporating their outputs via a learned Tool Context Encoder (TCE) and Trajectory Memory Buffer (TMB) trained end-to-end on protein-engineering objectives.
  • Contrastive Agent Policy Optimisation (CAPO) — a trajectory-level extension of direct preference optimisation that contrasts high-fitness trajectories with coherent oracle use against low-fitness or contradictory ones, teaching the model when oracle feedback is informative rather than merely imitating high-fitness sequences.

AgentPLM is initialised from the public ESM-2 650M checkpoint and trained in two phases (TCE/TMB only, then joint optimisation with layer-wise decay). Distinct from earlier ProtAgent, which freezes a GPT-4 backbone as a planner; AgentPLM trains the agent policy itself.

Validation

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. The authors claim mechanistic evidence of online error correction without explicit backtracking.

Notable results

  • 2.79× improvement in antibody top-10% hit rate over the strongest passive baseline.
  • +34% normalised k_cat/K_M on enzyme design.
  • Outperforms all baselines across the five benchmark tasks; authors attribute the gain to qualitatively different reasoning trajectories rather than additional compute.

Primary paper

Rahman & Rahman, “AgentPLM: Agentic Protein Language Models with Reasoning-Augmented Decoding for Protein Sequence Design,” arXiv:2606.02386 (Jun 2026); ICML 2026.

Other references

None.

Code

Not released.