PockLigGPT

PockLigGPT is a pocket-conditioned molecular generation framework based on GPT architectures and reinforcement learning (RL) for structure-based drug design.

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🌐 Access & Resources

🔬 Web Interface

👉 https://pockliggpt.streamlit.app

Researchers and companies can:

• submit PDB structures
• request molecule generation
• request full computational studies

💻 Code

👉 https://github.com/pvaras8/PockLigGPT_official

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🧬 Key Features

• 🧠 Protein-conditioned generation using sequence + embeddings
• 🧪 Docking-aware optimization via reinforcement learning
• 🔬 Structure-based drug design pipeline end-to-end
• ⚡ Scalable GPT-based architecture for molecular generation

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📦 Available Models

Pretraining

• ckpt_zink_M.pt → ZINC pretraining
• ckpt_zink_chembl.pt → ZINC + ChEMBL pretraining

Fine-tuned (CrossDocked)

• ckpt_zink_chembl_cross_sequence_add.pt → enhanced conditioning

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🚀 What Can It Do?

PockLigGPT enables:

• Generation of novel molecules conditioned on protein pockets
• Optimization of binding affinity using RL + docking
• Exploration of chemical space under structural constraints
• End-to-end workflows for early-stage drug discovery

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🧪 Example Workflow

1. Load a pretrained checkpoint
2. Provide protein pocket:
   • amino acid sequence
   • ProtT5 embeddings
   • docking grid
3. Run RL optimization
4. Generate candidate molecules

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⚙️ Technical Stack

• SELFIES molecular representation
• GPT-based autoregressive models
• ProtT5 protein embeddings
• AutoDock Vina (reward signal)
• Reinforcement learning (PPO-style)

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💼 Commercial Use

PockLigGPT is available for research use.

For:

• industrial applications
• large-scale screening
• full structure-based optimization workflows

👉 please contact via the web interface.

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⚠️ Limitations

• RL requires docking setup (external tools)
• Computationally intensive for large-scale runs
• Requires protein preprocessing

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📜 License

MIT License

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📄 Citation

Coming soon