SciCore-Mol: Augmenting Large Language Models with Pluggable Molecular Cognition Modules

Yuxuan Chen¹, Changwei Lv², Yunduo Xiao², Yukun Yan², Zheni Zeng^*3, and Zhiyuan Liu²

¹School of Electronic and Computer Engineering, Peking University, Shenzhen, China
²Tsinghua University, Beijing, China
³School of Intelligence Science and Technology, Nanjing University, Nanjing, China
^*Corresponding author: zengzn@nju.edu.cn

📖 Introduction

Large language models (LLMs) are increasingly popular in professional domains, while meet a fundamental cognitive tension when dealing with heterogeneous scientific data: LLMs are designed for discrete natural language symbolic sequences, whereas scientific entities represented by molecules are inherently topological and geometric. Forcing these structures into linear text inevitably results in information loss and semantic noise interferes with the LLM's cognitive reasoning.

We propose SciCore-Mol, a novel paradigm to augment the LLM with pluggable external cognitive modules, including a GVP encoder, a diffusion generator, and a numerical-sensitive Transformer (Reaction Transformer). This architecture preserves the general capabilities while providing specialized molecular perception for LLMs. With a two-stage alignment mechanism, external modules are invoked via special tokens and fused at the hidden-state level, enabling the LLM to deeply understand molecular information without sacrificing its core reasoning process.

⚙️ Setup

Prerequisites

• Python 3.10
• CUDA 12.1
• 8x A800/A100 80GB GPUs (recommended for full training)

Installation

git clone https://github.com/ChenYX24/SciCore-Mol.git
cd SciCore-Mol

# Option A: Install with uv (recommended)
pip install uv
uv sync
uv sync --extra graph      # GVP-GNN dependencies (torch-geometric, torch-scatter, torch-cluster)
uv sync --extra flashattn  # FlashAttention (requires CUDA)
uv sync --group train      # DeepSpeed for distributed training

# Option B: Install with pip
python -m venv .venv
source .venv/bin/activate
pip install -e .
pip install -e ".[graph]"       # optional: GVP-GNN
pip install -e ".[flashattn]"   # optional: FlashAttention
pip install deepspeed swanlab   # optional: distributed training

Environment Variables

cp configs/env.example.sh configs/env.sh
# Edit configs/env.sh to set your paths, then:
source configs/env.sh

Variable	Description
SCICORE_ROOT	Project root directory
MODEL_DIR	Base model directory (e.g., Qwen3-8B)
CHECKPOINT_DIR	Trained checkpoint directory
DATA_DIR	Training and evaluation data
GVP_CHECKPOINT	Pretrained GVP-GNN weights
OPENAI_API_KEY	API key for GPT baseline evaluation

🔧 Training

SciCore-Mol follows a three-stage training pipeline (see figure above):

Stage 1: Component Pre-training

Pre-train each component independently before joint training.

• GVP Encoder + MLP Adapter: Align GVP molecular embeddings to LLM hidden space.

  bash scripts/run/gvp_mlp_pretrain_qwen.sh
  

• Reaction Transformer (Layer2): Train on reaction data for yield prediction and embedding reconstruction.

  python scripts/layer2/train_layer2.py \
      --config scripts/layer2/layer2_train_config.yaml
  

Stage 2: Cross-Modal Alignment Training

Joint SFT training with all modules connected. The LLM learns to invoke external modules via special <mol> tokens.

# Configure training in configs/qwen3_sft_epoch2_1.yaml
# Uses DeepSpeed ZeRO-3 for multi-GPU training
torchrun --nproc_per_node=4 \
    cotrain_llm_diffusion/train_step1_llm.py \
    --config configs/qwen3_sft_epoch2_1.yaml

Key config fields (in configs/qwen3_sft_epoch2_*.yaml):

• paths.llm_name_or_path: Base LLM checkpoint
• paths.gnn_state_dict_path: Pretrained GVP weights
• paths.deepspeed_config: DeepSpeed config (ZeRO-2 or ZeRO-3)
• training.freeze_strategy: Control which modules are frozen/trainable

Stage 3: Task-Specific Fine-tuning

Fine-tune Layer2 (Reaction Transformer) on downstream tasks with configurable module freezing:

python scripts/layer2/train_layer2.py \
    --config scripts/layer2/layer2_train_config_stage2_v7b.yaml

After training, split the checkpoint into LLM and extra components:

python scripts/ckpt/split_llm_extras.py \
    --checkpoint_path ${CHECKPOINT_DIR}/your-checkpoint/ \
    --output_dir ${CHECKPOINT_DIR}/your-checkpoint/

📊 Evaluation

ChemBench4K (Product / Retrosynthesis / Yield / Captioning)

# Evaluate all 5 tasks with logprob scoring
bash scripts/run/run_chembench_all_tasks.sh

# Or run individual tasks:
python scripts/eval/eval_layer2_chembench.py \
    --checkpoint_dir ${CHECKPOINT_DIR}/your-checkpoint \
    --task product \
    --output_dir eval_results/chembench/

MMLU Chemistry Subsets (5 subjects)

python scripts/eval/eval_mmlu_interns1mini_5subsets.py \
    --model_path ${MODEL_DIR}/your-model \
    --output_dir eval_results/mmlu/

ORD Reaction Prediction (Full Pipeline)

# Run Layer2-LLM integrated pipeline
bash scripts/layer2_llm/run_full_pipeline.sh

# Score predictions
python scripts/postprocess/score_only.py \
    --pred_dir eval_results/ord/

SMolInstruct (7 molecular tasks)

# Automated multi-task evaluation with GPU scheduling
bash scripts/run/eval_smol_task_list.sh

Drug Optimization (ADMET scoring)

# LLM-based drug optimization
python eval/drug_optim/eval_admet.py \
    --config eval/drug_optim/config/llm_cpt_sft.yaml

# Diffusion-based drug optimization
python eval/drug_optim/eval_diffusion.py \
    --config eval/drug_optim/config/diffusion_sft.yaml

📄 Acknowledgement

• GVP-GNN — Geometric Vector Perceptron for molecular structure encoding
• LDMol — Latent Diffusion for molecular generation
• SMolInstruct — Molecular instruction tuning benchmark
• ChemBench — Chemistry benchmark suite

🥰 Citation

@article{chen2026scicoremol,
  title={SciCore-Mol: Augmenting Large Language Models with Pluggable Molecular Cognition Modules},
  author={},
  journal={arXiv preprint arXiv:XXXX.XXXXX},
  year={2026}
}

📧 Contact

If you have questions, suggestions, or bug reports, please open an issue or email:

chenyuxuan225@gmail.com

📜 License

This project is dual-licensed under MIT and Apache 2.0.