CONV1D-LSTM-BASED QSAR CLASSIFICATION MODEL FOR BACE1 INHIBITORS: A COMPREHENSIVE APPROACH WITH DESALTING, PAINS FILTERING AND DRUG-LIKENESS ANALYSIS
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Trianto Haryo Nugroho
Alhadi Bustamam
In recent years, the discovery of Beta-Secretase 1 (BACE1) enzyme inhibitors for more effective Alzheimer’s therapy has become a major focus, making in silico research to identify new inhibitors with minimal side effects increasingly essential. Ligand-Based Virtual Screening (LBVS) using Quantitative Structure–Activity Relationship (QSAR) methods offers a fast and cost-effective alternative to experimental assays. In this study, we propose a Conv1D-LSTM-based QSAR model as a novel approach for classifying BACE1 enzyme inhibitors, where Conv1D is employed for encoding molecular data and LSTM is used to classify compounds as active or inactive. The model is complemented by drug-likeness analysis based on Lipinski's Rule of Five to evaluate the therapeutic potential of candidate molecules. The dataset used includes 711 molecular structures, consisting of 278 active and 433 inactive compounds. Experimental results demonstrate that our model achieves a classification accuracy of 79.13%, with a sensitivity of 73.02%, specificity of 83.08%, and a Matthews Correlation Coefficient (MCC) of 56.38%.
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