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

• Published in: Journal of chemical information and modeling 2016-10, Vol.56 (10), p.1936-1949
• Main Authors: Subramanian, Govindan, Ramsundar, Bharath, Pande, Vijay, Denny, Rajiah Aldrin
• Format: Article
• Language: English
• Subjects: Amyloid Precursor Protein Secretases - antagonists & inhibitors; Amyloid Precursor Protein Secretases - chemistry; Amyloid Precursor Protein Secretases - metabolism; Aspartic Acid Endopeptidases - antagonists & inhibitors; Aspartic Acid Endopeptidases - chemistry; Aspartic Acid Endopeptidases - metabolism; Computer Simulation; Drug Discovery - methods; Humans; Ligands; Machine Learning; Models, Molecular; Neural Networks (Computer); Quantitative Structure-Activity Relationship; Small Molecule Libraries - chemistry; Small Molecule Libraries - pharmacology
• ISSN: 1549-9596; 1549-960X
• DOI: 10.1021/acs.jcim.6b00290

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.