🎯 PolyPharm

PolyPharm is a deep learning-based framework for multi-target drug design, capable of generating molecules with potential activities against multiple targets.

1️⃣ Directory Structure & Key Files

/                         ← Root directory
├── data                  ← Dataset, interaction scoring models, and multi-target scoring weights
│   ├── GSK3B+JNK3 
│   └── ROR_gamma+DHODH
├── results               ← Generated molecules from different methods on benchmark tasks
│   ├── gsk3β_jnk3 
│   └── rorγt_dhodh
├── model                 ← Model code
├── score_modules         ← Scoring utilities
│   ├── ESOL_Score
│   └── SA_Score
├── utils                     ← Utilities (multi-target scoring, data preprocessing, etc.)
├── environment.yml           ← Conda environment
├── train_chembl_baseline.py  ← Pre-training script
├── RL_generate.py            ← Fine-tuning script
├── generate.py               ← Molecule generation script

2️⃣ Results

✅ Pre-generated molecules are provided for download (including QED, SA, Docking score, LogP, Weight) along with comparison methods (some from AIxFuse open-source data):

• GSK3β|JNK3 benchmark task: results/gsk3β_jnk3/POLYGEN.csv
• RORγt|DHODH benchmark task: results/rorγt_dhodh/POLYGEN.csv

💡 To train from scratch, follow the steps below.

3️⃣ Quick Start

3.1 📥 Clone Repository & Download Dataset

git clone https://github.com/Yozu-Roo/POLYGEN.git

• Dataset (~1.69GB) download via Google Drive
• Or download via Baidu Drive
• Or contact via email: niuziru@stu.xmu.edu.cn

After download, extract and place the data/ folder at the root directory.

---

3.2 ⚙️ Install Conda Environment

Recommended Python 3.8:

conda env create -f environment.yml
conda activate polygen

---

3.3 🏋️‍♂️ Pre-training

python train_chembl_baseline.py

• For multiple GPUs, adjust CUDA_VISIBLE_DEVICES in the script
• Model weights are saved in pretrain_output/ during training

---

3.4 🔧 Fine-tuning

GSK3β|JNK3 benchmark task:

python RL_generate.py \
    --target_name GSK3B JNK3 \
    --output_dir ./finetune_output_GJ \
    --model_path ./pretrain_output/rs_mapping/fold0_epoch32.pth \
    --tokenizer_path ./pretrain_output/rs_mapping/tokenizer.pkl \
    --n_mol 10000 \
    --device cuda \
    --batch_size 512 \
    --seed 42 \
    --threshold 0.60 \
    --n_epochs 20 \
    --optimize_n_epochs 5 \
    --save_frequency 10 \
    --save_payloads \
    --keep_top 10000

RORγt|DHODH benchmark task:

python RL_generate.py \
    --target_name ROR_gamma DHODH \
    --output_dir ./finetune_output_RD \
    --model_path ./pretrain_output/fold0_epoch32.pth \
    --tokenizer_path ./pretrain_output/tokenizer.pkl \
    --n_mol 30000 \
    --device cuda \
    --batch_size 512 \
    --seed 42 \
    --threshold 0.7 \
    --n_epochs 20 \
    --optimize_n_epochs 5 \
    --save_frequency 10 \
    --save_payloads \
    --keep_top 10000

• --tokenizer_path is your pre-trained model path
• --threshold is the threshold for screening elite molecules
• --n_epochs is the fine-tuning epochs
• --optimize_n_epochs is the optimization epochs
• --n_mol is the number of molecules sampled each epoch
• Generated results and model weights are saved in finetune_output_*/
• Multi-GPU users may modify CUDA_VISIBLE_DEVICES

---

3.5 🧪 Molecule Generation

python generate.py \
    --target_name ROR_gamma DHODH \  
    --output_dir ./generate_output_RD \
    --model_path ./finetune_output_RD/epoch_15_finetuned_model.pth \  
    --tokenizer_path ./pretrain_output/tokenizer.pkl \
    --n_mol 10000 \
    --device cuda \
    --filter \
    --batch_size 512 \
    --seed 42

• --target_name is the benchmark task and can be replaced with GSK3B JNK3
• --model_path is your fine-tuned model path
• --n_mol is the number of generated molecules
• Generated molecules are saved in generate_output_*/

---

4️⃣ Tips🌟

• Ensure all paths are correct to avoid file-not-found errors
• GPU significantly speeds up training and generation
• Docking tool: AutoDock Vina or using Vina-GPU speeds up
• Retrosynthesis tool: AiZynthFinder