MolecularDockingEngine

Introduction

Structure-based virtual screening by molecular docking is a cornerstone of early-stage drug discovery, enabling rapid prioritization of compound libraries against protein targets. We present MolecularDockingEngine, a pure-Python implementation of the complete virtual screening workflow.

Methods

Binding Pocket Detection

Probe sphere rolling algorithm: probe spheres (radius 1.4 Å) are placed around protein surface atoms. Probes with 8-25 nearby protein atoms (within 5 Å) are classified as pocket probes. Pocket probes are clustered by hierarchical clustering (complete linkage, distance threshold 4 Å).

Ligand Conformer Generation

For each compound, 5 random conformers are generated by placing heavy atoms within the binding pocket (Gaussian distribution, σ = 0.4 × pocket radius). The best-scoring conformer is retained.

Scoring Function

E_total = E_vdW + E_elec + E_hbond + E_desolvation

• E_vdW: Lennard-Jones 6-12 potential with combined radii
• E_elec: Coulomb electrostatics with distance-dependent dielectric (ε = r²)
• E_hbond: -0.8 kcal/mol per N-O pair within 3.5 Å
• E_desolvation: +0.05 kcal/mol per buried polar atom

ADMET Filtering

Lipinski's rule of 5 (MW≤500, logP≤5, HBD≤5, HBA≤10) + TPSA<140 Ų + rotatable bonds≤10 + logP>0.

Results

• 280-residue protein, 1 binding pocket (volume ≈500 ų)
• 200 compounds screened (186 Lipinski-compliant, 93%)
• Score range: -14.41 to -1.49 kcal/mol (mean -7.00 ± 2.50)
• 20 hits (top 10%, threshold -10.26 kcal/mol)
• Best compound: CPMD0154 (-14.41 kcal/mol, MW=568, logP=-0.2)
• Best ADMET-pass: CPMD0008 (-12.75 kcal/mol, MW=487, logP=3.7, TPSA=67 Ų)
• 12 ADMET-compliant hits, 1 binding mode cluster

Conclusion

MolecularDockingEngine provides a complete, executable virtual screening pipeline in pure Python, enabling reproducible structure-based drug discovery without specialized docking software.

Code

https://github.com/BioTender-max/MolecularDockingEngine

pip install numpy scipy matplotlib
python molecular_docking_engine.py