• Tanmoy Jana, Debasree Sarkar, Debayan Ganguli, Sandip Kumar Mukherjee, Rahul Shubhra Mandal, and Santasabuj Das.
• Division of Clinical Medicine, ICMR-National Institute of Cholera and Enteric Diseases, Kolkata, West Bengal, India.
• Indian J Med Res. 2024 Jan 1; 159 (1): 789078-90.

Background ObjectivesDiscovery of new antibiotics is the need of the hour to treat infectious diseases. An ever-increasing repertoire of multidrug-resistant pathogens poses an imminent threat to human lives across the globe. However, the low success rate of the existing approaches and technologies for antibiotic discovery remains a major bottleneck. In silico methods like machine learning (ML) deem more promising to meet the above challenges compared with the conventional experimental approaches. The goal of this study was to create ML models that may be used to successfully predict new antimicrobial compounds.MethodsIn this article, we employed eight different ML algorithms namely, extreme gradient boosting, random forest, gradient boosting classifier, deep neural network, support vector machine, multilayer perceptron, decision tree, and logistic regression. These models were trained using a dataset comprising 312 antibiotic drugs and a negative set of 936 non-antibiotic drugs in a five-fold cross validation approach.ResultsThe top four ML classifiers (extreme gradient boosting, random forest, gradient boosting classifier and deep neural network) were able to achieve an accuracy of 80 per cent and above during the evaluation of testing and blind datasets.Interpretation ConclusionsWe aggregated the top performing four models through a soft-voting technique to develop an ensemble-based ML method and incorporated it into a freely accessible online prediction server named ABDpred ( http://clinicalmedicinessd.com.in/abdpred/ ).Copyright © 2024 Copyright: © 2024 Indian Journal of Medical Research.

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