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
        │
        ▼
┌──────────────────────────────────────────────┐
│  EnzyGen2 — Generate protein sequences       │
└──────────────────────────────────────────────┘
        │
        ▼
┌──────────────────────────────────────────────┐
│  ESMFold — Predict 3D structures (GPU)      │
└──────────────────────────────────────────────┘
        │
        ▼
┌──────────────────────────────────────────────┐
│  AutoDock Vina — Dock and score            │
└──────────────────────────────────────────────┘
        │
        ▼
   Combined ranking + JSON/CSV report

Installation

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.sh

Usage

python3 cli.py --ligand "CSCC(=O)Nc1cc(-c2ccnc(N)c2)ccc1OCCOc1ccc(OCCO)cc1" --motif 10665 --num 10
streamlit run app.py --server.port 8501

Key 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