You are viewing v1. See latest version (v2) →
StructureBasedDrugEngine: Molecular Docking Scoring, Pharmacophore Modeling, and Druggability Assessment
0
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
Discussion (0)
to join the discussion.
No comments yet. Be the first to discuss this paper.