MetaboLLM

MetaboLLM is a family of metabolomics-specialized large language models designed to integrate biochemical knowledge across heterogeneous resources and support metabolite-, pathway-, reaction-, and enzyme-centered reasoning and description generation.

MetaboLLM was developed through continual pretraining, supervised fine-tuning, and structured retrieval using harmonized biochemical knowledge from KEGG, HMDB, PubChem, and SMPDB.

Highlights

• Integrated knowledge covering 237,243 metabolites, 2,359 pathways, 12,323 reactions, and 5,993 enzymes
• Released four MetaboLLM model variants across Qwen, Gemma, and Llama backbones
• Constructed a metabolomics benchmark containing 17 tasks, 6,000 training examples, and 4,200 test examples
• Evaluated factual knowledge, class identification, biochemical relationships, and description generation
• Compared MetaboLLM against corresponding base models and five publicly available medical language models
• Evaluated transfer on the independently developed MetaBench benchmark

Resources

Model Family

MetaboLLM-Qwen3-4B served as the primary model in the associated study. All released repositories contain adapter weights and require the corresponding base model.

Integrated Biochemical Knowledge

MetaboLLM was developed from a unified resource integrating complementary information from:

• KEGG for metabolites, reactions, enzymes, and pathways
• HMDB for human metabolites, biological roles, chemical taxonomy, and compound descriptions
• PubChem for chemical structures, identifiers, molecular properties, and compound descriptions
• SMPDB for curated pathways and physiological descriptions

The harmonized resource contains:

MetaboLLM Benchmark

The benchmark contains 17 tasks organized into four complementary categories.

The benchmark evaluates:

• molecular identity and formula knowledge
• metabolite and reaction class recognition
• metabolite–pathway relationships
• metabolite–reaction relationships
• reaction–enzyme relationships
• metabolite, pathway, and enzyme description generation
• structure-rich and structure-poor metabolite description generation

Benchmark Results

Biochemical Knowledge and Structured Relationships

Mean accuracy across 12 multiple-choice and short-answer tasks.

All four MetaboLLM variants outperformed their corresponding unadapted backbones and all evaluated medical language models. MetaboLLM-Qwen3-4B achieved the highest mean accuracy at 79.1%, compared with 65.7% for the strongest evaluated medical model and 66.5% for its corresponding base model.

Biochemical Description Generation

BERTScore-F1 on a 0–100 scale.

MetaboLLM-Qwen3-4B achieved the highest BERTScore-F1 across all five description-generation tasks.

External Benchmark Transfer

MetaboLLM was also evaluated on MetaBench, an independently developed public metabolomics benchmark.

• MetaboLLM-Qwen3-8B achieved the highest Knowledge MCQA accuracy at 56.42%
• MetaboLLM-Qwen3-4B achieved the highest pathway-description scores, including 85.19 BERTScore-F1, 25.83 ROUGE-L-F1, and 17.61 BLEU-2

These results demonstrate transfer beyond the internally constructed MetaboLLM benchmark.

Quick Start

Install the required packages:

pip install -U transformers peft accelerate torch

Example using MetaboLLM-Qwen3-4B:

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

base_model_id = "Qwen/Qwen3-4B-Instruct-2507"
adapter_id = "MetaboLLM/MetaboLLM-Qwen3-4B"

tokenizer = AutoTokenizer.from_pretrained(adapter_id)

base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype="auto",
    device_map="auto",
)

model = PeftModel.from_pretrained(
    base_model,
    adapter_id,
)

messages = [
    {
        "role": "user",
        "content": "What is the biochemical role of pyruvate?"
    }
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)

with torch.no_grad():
    outputs = model.generate(
        inputs,
        max_new_tokens=256,
        do_sample=False,
    )

response = tokenizer.decode(
    outputs[0][inputs.shape[-1]:],
    skip_special_tokens=True,
)

print(response)

Please check the corresponding model card for the exact base-model identifier and model-specific loading instructions.

Loading the Benchmark

from datasets import load_dataset

dataset = load_dataset(
    "MetaboLLM/MetaboLLM-Benchmark",
    "Knowledge_recall",
)

print(dataset["test"][0])

Available configurations:

• Knowledge_recall
• Class_identification
• Relation_identification
• Description_generation

Intended Uses

MetaboLLM is intended for research involving:

• metabolomics-specific question answering
• biochemical knowledge recall
• metabolite and reaction class identification
• metabolite–pathway and reaction–enzyme relationship identification
• metabolite, pathway, and enzyme description generation
• evaluation of metabolomics-specialized language models

Limitations

• MetaboLLM may generate incorrect or unsupported biochemical statements.
• Performance outside metabolomics and biochemical knowledge tasks has not been comprehensively evaluated.
• Outputs should be independently verified and should not be used for clinical decision-making.

Licenses

Each MetaboLLM repository is distributed according to its model card and license notice.

• Use of each adapter remains subject to the license and terms of its corresponding base model.
• Benchmark components created by the MetaboLLM authors are distributed under the terms described in the benchmark repository.
• Third-party and source-derived biochemical content remains subject to the licenses and terms of the original providers.

Please review the license files in the relevant model and dataset repositories before use.

Resource Maintainers

• Dohyun Ku
• Min Gu Kwak