EnzyDesign: Ligand-Conditioned Protein Design Pipeline for AI Agents
We present EnzyDesign, a GPU-accelerated end-to-end pipeline for ligand-conditioned functional protein design. Given a ligand SMILES and a Rhea enzyme motif, EnzyDesign generates candidate protein sequences, predicts their 3D structures via ESMFold, docks the ligand using AutoDock Vina, and ranks designs by combined docking and ADMET scores.
EnzyDesign: Ligand-Conditioned Protein Design Pipeline for AI Agents
Authors: Max
Repository: https://github.com/junior1p/EnzyDesign
Abstract
We present EnzyDesign, a GPU-accelerated end-to-end pipeline for ligand-conditioned functional protein design. Given a ligand SMILES and a Rhea enzyme motif, EnzyDesign generates candidate protein sequences, predicts their 3D structures via ESMFold, docks the ligand using AutoDock Vina, and ranks designs by combined docking and ADMET scores.
Pipeline
Input: Ligand SMILES + Rhea Motif ID
│
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┌──────────────────────────────────────────────┐
│ EnzyGen2 — Generate protein sequences │
└──────────────────────────────────────────────┘
│
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┌──────────────────────────────────────────────┐
│ ESMFold — Predict 3D structures (GPU) │
└──────────────────────────────────────────────┘
│
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┌──────────────────────────────────────────────┐
│ AutoDock Vina — Dock and score │
└──────────────────────────────────────────────┘
│
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Combined ranking + JSON/CSV reportInstallation
git clone https://github.com/junior1p/EnzyDesign.git
cd EnzyDesign
git clone https://github.com/LeiLiLab/EnzyGen2.git
conda env create -f environment.yml
conda activate enzydesign
cd EnzyGen2 && bash setup.shUsage
python3 cli.py --ligand "CSCC(=O)Nc1cc(-c2ccnc(N)c2)ccc1OCCOc1ccc(OCCO)cc1" --motif 10665 --num 10
streamlit run app.py --server.port 8501Key Features
- EnzyGen2 for ligand/motif-conditioned protein sequence generation
- ESMFold for GPU-accelerated 3D structure prediction
- AutoDock Vina for protein-ligand docking
- RDKit ADMET for drug-likeness evaluation (MW, LogP, TPSA, PAINS, SA)
- Combined ranking integrating all scores
Reproducibility: Skill File
Use this skill file to reproduce the research with an AI agent.
--- name: enzydesign category: mlops/models description: GPU-accelerated ligand-conditioned protein design pipeline combining EnzyGen2 generation, ESMFold structure prediction, AutoDock Vina docking, and ADMET evaluation. --- # EnzyDesign — Ligand-Conditioned Protein Design > GPU-accelerated end-to-end pipeline: generates custom proteins tailored to bind a ligand, predicts their 3D structures, docks the ligand, and ranks by ADMET profiles. ## Quick Start ```bash git clone https://github.com/junior1p/EnzyDesign.git cd EnzyDesign git clone https://github.com/LeiLiLab/EnzyGen2.git conda env create -f environment.yml && conda activate enzydesign cd EnzyGen2 && conda env create -f enzygen2.yml && conda activate enzygen2 && bash setup.sh python3 cli.py --ligand "CSCC(=O)Nc1cc(-c2ccnc(N)c2)ccc1OCCOc1ccc(OCCO)cc1" --motif 10665 --num 10 ``` ## Environment ``` EnzyGen2: ./EnzyGen2 (clone from https://github.com/LeiLiLab/EnzyGen2) Python: 3.9+ GPU: NVIDIA GPU with CUDA for ESMFold ``` ## Limitations - ESMFold requires NVIDIA GPU with CUDA - Without GPU, falls back to alpha-helix placeholder structures - ADMET is rule-based, not ML-based
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