protein-ligand-design

🧪 Protein-Ligand Design — LoRA adapter for poolside/Laguna-XS.2

poolside Laguna Hackathon submission — Team JAMMY. A LoRA adapter trained with reinforcement learning (GRPO) to make poolside/Laguna-XS.2 reason like a bench computational chemist / protein engineer: measure with tools, then commit an answer.

This is the trained adapter that goes with our environment and dataset:

➡️ Gym / dataset: poolside-laguna-hackathon/protein-ligand-design

The gym hands the model a molecule or protein plus a scientist's question, and the model must call CPU-only cheminformatics/proteomics tools (RDKit + Biopython) to measure the answer before committing. The reward is answer correctness only, and every ground-truth answer is computed by those same tools, so scoring is exact.

What this adapter is

LoRA is applied to the attention projections and the MoE expert MLPs, which is why the adapter is large (~4.6 GB) despite being rank-16.

Training

Trained on Prime Intellect Hosted Training — shared run dashboard:

• Algorithm: GRPO
• Reward: binary final-answer correctness (1.0 correct / 0.0 wrong) — using tools is the means, never the reward
• Learning rate: 1e-5
• Rollouts per example: 16
• Batch size: 128
• Max tokens: 4096, thinking enabled
• Steps: 80 (full run); the final checkpoint is the (tied-)best on eval.

What training changed

On a held-out set of 60 questions, eval accuracy (avg@1) rose from the base model's 91.7% to 96.7% — a real +5-point gain, with the final checkpoint tied for best. Training was stable throughout: reward held around 0.9 (normal GRPO variance, dipping to ~0.65 and recovering), completions stayed ~900 tokens (no ballooning), and there were no truncated or failed rollouts.

A concrete example. One question the base model got wrong before training:

For a kinase backbone-carbonyl halogen bond, is 4-bromo-7-azaindole (Brc1ccc2[nH]ccc2n1) a candidate — it must carry a heavy halogen (Cl/Br/I) for the halogen bond, an aromatic ring for π-stacking, and LogP between 1 and 3? (correct answer: yes)

The base model ran the tools (substructure matches for the halogen and ring, plus descriptors) but committed the wrong verdict. This is the kind of tool-grounded judgement the adapter sharpens.

Honest framing: the base model is already strong on this set (~92%), so the available headroom was modest — the adapter captured most of it.

Choosing the training recipe

The stable hyperparameters above didn't come for free — they're the output of a sweep on a precursor environment, allan/science-gym-bio. The lesson: learning rate is the stability lever (5e-5 peaks then collapses; 1e-5 holds), and larger rollout groups (8 → 16) cut GRPO advantage variance. That recipe — LR 1e-5, 16 rollouts/example, thinking on — is what we carried into the protein-ligand run.

Usage

import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = "poolside/Laguna-XS.2"
tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, torch_dtype=torch.bfloat16, device_map="auto")
model = PeftModel.from_pretrained(model, "poolside-laguna-hackathon/protein-ligand-design")

For the full tool-use evaluation loop, install and run the gym:

prime env install jdthewlis/protein-ligand-design
prime eval run jdthewlis/protein-ligand-design -m <your-deployment> -n 20 -r 3

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Built by Team JAMMY for the poolside Laguna hackathon. Trained with GRPO on Prime Intellect Hosted Training; environment questions generated with Claude Opus 4.8.