Antibacterial Activity Prediction of Plant Secondary Metabolites Based on a Combined Approach of Graph Clustering and Deep Neural Network

• Published in: Molecular informatics 2022-07, Vol.41 (7), p.e2100247-n/a
• Main Authors: Karim, Mohammad Bozlul, Kanaya, Shigehiko, Altaf‐Ul‐Amin, Md
• Format: Article
• Language: English
• Subjects: Anti-Bacterial Agents - pharmacology; Antibacterial; Antibacterial activity; Artificial neural networks; Chemical compounds; Chemical reactions; Classification; Cluster; Cluster Analysis; Clustering; Computer applications; Correlation; DNN; Drug development; Fingerprint; Fingerprints; Graph; Metabolite; Metabolites; Neural networks; Neural Networks, Computer; Secondary metabolites; Similarity; Training
• ISSN: 1868-1743; 1868-1751; 1868-1751
• DOI: 10.1002/minf.202100247

The plants produce numerous types of secondary metabolites which have pharmacological importance in drug development for different diseases. Computational methods widely use the fingerprints of the metabolites to understand different properties and similarities among metabolites and for the prediction of chemical reactions etc. In this work, we developed three different deep neural network models (DNN) to predict the antibacterial property of plant metabolites. We developed the first DNN model using the fingerprint set of metabolites as features. In the second DNN model, we searched the similarities among fingerprints using correlation and used one representative feature from each group of highly correlated fingerprints. In the third model, the fingerprints of metabolites were used to find structurally similar chemical compound clusters. Form each cluster a representative metabolite is selected and made part of the training dataset. The second model reduced the number of features where the third model achieved better classification results for test data. In both cases, we applied the simple graph clustering method to cluster the corresponding network. The correlation‐based DNN model reduced some features while retaining an almost similar performance compared to the first DNN model. The third model improves classification results for test data by capturing wider variance within training data using graph clustering method. This third model is somewhat novel approach and can be applied to build DNN models for other purposes.