Computational Modeling of β‑Secretase 1 (BACE-1) Inhibitors Using Ligand Based Approaches

The binding affinities
(IC50) reported for diverse structural
and chemical classes of human β-secretase 1 (BACE-1) inhibitors
in literature were modeled using multiple in silico ligand based modeling
approaches and statistical techniques. The descriptor space encompasses
simple binary molecular fingerprint, one- and two-dimensional constitutional,
physicochemical, and topological descriptors, and sophisticated three-dimensional
molecular fields that require appropriate structural alignments of
varied chemical scaffolds in one universal chemical space. The affinities
were modeled using qualitative classification or quantitative regression
schemes involving linear, nonlinear, and deep neural network (DNN)
machine-learning methods used in the scientific literature for quantitative–structure
activity relationships (QSAR). In a departure from tradition, ∼20%
of the chemically diverse data set (205 compounds) was used to train
the model with the remaining ∼80% of the structural and chemical
analogs used as part of an external validation (1273 compounds) and
prospective test (69 compounds) sets respectively to ascertain the
model performance. The machine-learning methods investigated herein
performed well in both the qualitative classification (∼70%
accuracy) and quantitative IC50 predictions (RMSE ∼
1 log). The success of the 2D descriptor based machine learning approach
when compared against the 3D field based technique pursued for hBACE-1 inhibitors provides a strong impetus for systematically
applying such methods during the lead identification and optimization
efforts for other protein families as well.