ABDpred: Prediction of active antimicrobial compounds using supervised machine learning techniques
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Date
2024-01
Journal Title
Journal ISSN
Volume Title
Publisher
Wolters Kluwer – Medknow
Abstract
Background & objectives: Discovery 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. Methods: In 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. Results: The 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 & conclusions: We 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 (https://clinicalmedicinessd.com.in/abdpred/).
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Keywords
Extreme gradient boosting, five-fold cross-validation, machine learning, multidrug resistant, neural network, random forest
Citation
Jana Tanmoy, Sarkar Debasree, Ganguli Debayan, Mukherjee Sandip Kumar, Mandal Rahul Shubhra, Das Santasabuj. ABDpred: Prediction of active antimicrobial compounds using supervised machine learning techniques. Indian Journal of Medical Research. 2024 Jan; 159(1): 78-90