StructureBasedDrugEngine: Molecular Docking Scoring, Pharmacophore Modeling, and Druggability Assessment
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Structure-based drug design leverages protein 3D structures to identify and optimize small molecule binders. We present StructureBasedDrugEngine, a pure-Python pipeline for structure-based drug discovery. The engine implements molecular docking scoring (AutoDock Vina-style), pharmacophore model generation, druggability assessment (fpocket-style), ADMET property prediction, and scaffold diversity analysis. Applied to 50 targets × 1000 compounds, the pipeline identifies 22 druggable targets (44%), 324 hits (32.4%), best docking score=−14.33 kcal/mol, and mean QED=0.55.
Introduction
Structure-based drug design uses protein crystal structures to guide compound optimization. Molecular docking predicts binding poses and affinities. Druggability assesses whether a binding site can accommodate drug-like molecules.
Methods
Docking Score
Vina score = f_inter + f_intra. f_inter = Σ (steric + H-bond + hydrophobic + electrostatic).
Druggability
fpocket: pocket volume > 300 Ų, druggability score > 0.5.
ADMET
QED = exp(Σ w_i × log(d_i(x_i))). Lipinski: MW<500, logP<5, HBD<5, HBA<10.
Results
Druggable=22/50 (44%). Hits=324 (32.4%). Best docking=−14.33. Mean QED=0.55.
Code Availability
Reproducibility: Skill File
Use this skill file to reproduce the research with an AI agent.
--- name: structure-based-drug-engine description: Molecular docking scoring, pharmacophore modeling, and protein pocket druggability assessment allowed-tools: Bash(python *) --- # Steps to reproduce 1. Clone the repository: ```bash git clone https://github.com/BioTender-max/StructureBasedDrugEngine cd StructureBasedDrugEngine ``` 2. Install dependencies: ```bash pip install numpy scipy matplotlib ``` 3. Run the analysis: ```bash python structure_based_drug_engine.py ``` 4. Output: `structure_based_drug_engine_dashboard.png` — a 9-panel dark-theme dashboard summarizing all key results. > Requires Python 3.8+. No external data downloads needed — all data is synthetically generated with seed=42 for full reproducibility.
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