Benchmarking 2D/3D/MD-QSAR Models for Imatinib Derivatives: How Far Can We Predict?

Imatinib,
a 2-phenylaminopyridine-based BCR-ABL tyrosine kinase
inhibitor, is a highly effective drug for treating Chronic Myeloid
Leukemia (CML). However, cases of drug resistance are constantly emerging
due to various mutations in the ABL kinase domain; thus, it is crucial
to identify novel bioactive analogues. Reliable QSAR models and molecular
docking protocols have been shown to facilitate the discovery of new
compounds from chemical libraries prior to experimental testing. However,
as the vast majority of QSAR models strictly relies on 2D descriptors,
the rise of 3D descriptors directly computed from molecular dynamics
simulations offers new opportunities to potentially augment the reliability
of QSAR models. Herein, we employed molecular docking and molecular
dynamics on a large series of Imatinib derivatives and developed an
ensemble of QSAR models relying on deep neural nets (DNN) and hybrid
sets of 2D/3D/MD descriptors in order to predict the binding affinity
and inhibition potencies of those compounds. Through rigorous validation
tests, we showed that our DNN regression models achieved excellent
external prediction performances for the pKi data set (n = 555, R2 ≥ 0.71. and MAE ≤ 0.85), and the pIC50 data set (n = 306, R2 ≥ 0.54. and MAE ≤
0.71) with strict validation protocols based on external test sets
and 10-fold native and nested cross validations. Interestingly, the
best DNN and random forest models performed similarly across all descriptor
sets. In fact, for this particular series of compounds, our external
test results suggest that incorporating additional 3D protein–ligand
binding site fingerprint, descriptors, or even MD time-series descriptors
did not significantly improve the overall R2 but lowered
the MAE of DNN QSAR models. Those augmented models could still help
in identifying and understanding the key dynamic protein–ligand
interactions to be optimized for further molecular design.