Prioritizing Virtual Screening with Interpretable Interaction Fingerprints

Machine learning-based drug discovery success depends
on molecular
representation. Yet traditional molecular fingerprints omit both the
protein and pointers back to structural information that would enable
better model interpretability. Therefore, we propose LUNA, a Python
3 toolkit that calculates and encodes protein–ligand interactions
into new hashed fingerprints inspired by Extended Connectivity FingerPrint
(ECFP): EIFP (Extended Interaction FingerPrint), FIFP (Functional
Interaction FingerPrint), and Hybrid Interaction FingerPrint (HIFP).
LUNA also provides visual strategies to make the fingerprints interpretable.
We performed three major experiments exploring the fingerprints’
use. First, we trained machine learning models to reproduce DOCK3.7
scores using 1 million docked Dopamine D4 complexes. We found that EIFP-4,096 performed (R2 = 0.61)
superior to related molecular and interaction fingerprints. Second,
we used LUNA to support interpretable machine learning models. Finally,
we demonstrate that interaction fingerprints can accurately identify
similarities across molecular complexes that other fingerprints overlook.
Hence, we envision LUNA and its interface fingerprints as promising
methods for machine learning-based virtual screening campaigns. LUNA
is freely available at https://github.com/keiserlab/LUNA.