BACE1 QSAR Modeling Using RDKit and Machine Learning

A machine learning-based QSAR(Quantitative Structure–Activity Relationship) model developed to predict the inhibitory activity (pIC50) of BACE1 inhibitors using molecular descriptors calculated with RDKit.

Overview:

Beta-secretase 1 (BACE1) is an important therapeutic target in Alzheimer's disease because it initiates the cleavage of amyloid precursor protein, leading to the formation of β-amyloid peptides. Computational approaches such as QSAR can be used to relate molecular properties to biological activity and assist in early-stage drug discovery. This project implements a basic QSAR workflow using publicly available BACE1 bioactivity data from ChEMBL.

Datasets used:

1. ChEMBL Database
2. Target : BACE1
3. Bioactivity: IC50
4. Response variable: pIC50

QSAR Pipeline:

ChEMBL Database │ ▼ Download BACE1 Bioactivity Data │ ▼ Filter IC50 Measurements │ ▼ Remove Duplicate Compounds │ ▼ Convert SMILES → Molecular Structures │ ▼ Calculate Molecular Descriptors (MolWt, LogP, TPSA, HBD, HBA, RotBonds) │ ▼ Generate pIC50 Values │ ▼ Train-Test Split │ ▼ Random Forest Regression │ ▼ Model Evaluation (R², MAE, RMSE)

Model performance:

Metric	Value
R² Score	0.429
MAE	0.734
RMSE	0.941

Planned enhancements include:

1. RDKit descriptor set (~200 descriptors)
2. Morgan (ECFP4) fingerprints
3. Feature selection
4. Hyperparameter optimization
5. Cross-validation
6. External validation
7. Model comparison (Random Forest, XGBoost, SVR)
8. Applicability domain analysis
9. Virtual screening of novel compoun