Gemma4EC-9B-Predict

LoRA adapter for google/txgemma-9b-predict that predicts the Enzyme Commission (EC) number of a biochemical reaction from its SMILES, down to the sub-subclass level.

• Input: a reaction SMILES, e.g. CCO.O=O>>CC=O.OO
• Output: an EC number, e.g. 1.01.03
• Code, preprocessing and benchmarks: github.com/PlanesLab/Gemma4EC

Usage

The base model is gated: request access to google/txgemma-9b-predict, then huggingface-cli login.

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

base = "google/txgemma-9b-predict"
tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, torch_dtype=torch.bfloat16, device_map="auto")
model = PeftModel.from_pretrained(model, "PlanesLab/Gemma4EC-9B-Predict").eval()

PROMPT = (
    "Instructions: Given a reaction SMILES, predict the first three parts of its "
    "Enzyme Commission (EC) number (class.subclass.sub-subclass).\n"
    "EC numbers are four-part hierarchical codes (e.g., 1.3.2.4) describing the type "
    "of enzyme and its reaction mechanism.\n"
    "Only output the first three digits (e.g., 1.03.02) representing the enzyme's "
    "general class, subclass, and sub-subclass.\n"
    "Reaction SMILES: {reaction_smiles}\nAnswer:"
)

inputs = tokenizer(PROMPT.format(reaction_smiles="CCO.O=O>>CC=O.OO"), return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=8, do_sample=False)
print(tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True).strip())

Use this exact prompt. Preprocess SMILES with preprocess_smiles.py from the repository first; batch prediction and chat explanations are also there.

Training

LoRA (r = 16, alpha = 16) on q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj, fine-tuned on ECReact.

Citation

Please cite Gemma4EC (see the repository) and TxGemma.

License

Adapter weights: Apache-2.0. TxGemma weights: Health AI Developer Foundations terms.