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 IC50predictions (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.
VMRD Global Discovery, Zoetis , 333 Portage Street, Kalamazoo, Michigan 49007, United States.
Govindan
G
Subramanian
Bharath
B
Ramsundar
Vijay
V
Pande
Worldwide Medicinal Chemistry, Pfizer Inc. , 610 Main Street, Cambridge, Massachusetts 02139, United States.
Rajiah Aldrin
RA
Denny
D053829:Amyloid Precursor Protein Secretases / Q000037:antagonists & inhibitors* / Q000737:chemistry / Q000378:metabolism; D016282:Aspartic Acid Endopeptidases / Q000037:antagonists & inhibitors* / Q000737:chemistry / Q000378:metabolism; D003198:Computer Simulation; D055808:Drug Discovery / Q000379:methods; D006801:Humans; D008024:Ligands; D000069550:Machine Learning; D008958:Models, Molecular; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship; D054852:Small Molecule Libraries / Q000737:chemistry / Q000494:pharmacology
D008024:Ligands; D054852:Small Molecule Libraries; D053829:Amyloid Precursor Protein Secretases; D016282:Aspartic Acid Endopeptidases; C509280:BACE1 protein, human