Application of artificial neural networks to prediction of new substances with antimicrobial activity against<i>Escherichia coli</i>

Badura, Anna; Krysiński, Jerzy; Nowaczyk, Alicja; Buciński, Adam

doi:10.1111/jam.14763

Cited by 16 publications

(7 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, some models are focused on individual classes of compounds 8 25 , and hence, are not applicable to diverse compound libraries. Others, being restricted to individual organisms 13 26 , are not helpful for the discovery of broad-spectrum antimicrobials. Although models for the prediction of antibacterial drug from heterogeneous molecules exist 6 11 , significant scope remains for their improvement through the use of advanced ML algorithms such as XGBoost, RF and DNN.…”

Section: Discussionmentioning

confidence: 99%

ABDpred: Prediction of active antimicrobial compounds using supervised machine learning techniques

Jana,

Sarkar,

Ganguli

et al. 2024

Indian Journal of Medical Research

View full text Add to dashboard Cite

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 ( http://clinicalmedicinessd.com.in/abdpred/ ).

show abstract

Section: Discussionmentioning

confidence: 99%

ABDpred: Prediction of active antimicrobial compounds using supervised machine learning techniques

Jana,

Sarkar,

Ganguli

et al. 2024

Indian Journal of Medical Research

View full text Add to dashboard Cite

show abstract

“…Molecular descriptors play an important role in pharmaceutical sciences, and have wide applications in the development of the quantitative structure-activity relationship (QSAR) models, for predicting both physicochemical properties and pharmacological and toxicological effects [8,9,20,[22][23][24][25].…”

Section: Molecular Descriptorsmentioning

confidence: 99%

Application of artificial neural networks for predicting imidazole derivatives antimicrobial activity against Enterococcus faecalis

Badura

Krysiński

Nowaczyk

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The paper discusses artificial neural networks (ANNs) as a useful deep learning (DL) method to assist researchers in the search for new therapeutic and disinfectant substances. Two ANN models have been designed to predict the biological activity of the compounds based on their physicochemical properties and their structure. The said activity was tested against Enterococcus faecalis bacteria on a series of 140 imidazole derivatives. The regression model designed, predicted the minimum growth inhibitory concentration of E. faecalis (regression model: training data R = 0.91; test data R = 0.91; validation data R = 0.97). The classification model, on the other hand, divided the tested compounds into active or inactive against the tested microorganism predictive (classification accuracy: 92.86%). The exponential demand for new compounds in the pharmaceutical industry, requires alternative experimental methods to reduce the time and cost of development. Therefore, this paper proposes ANN as an alternative to standard techniques for predicting complex biological phenomena.

show abstract

Section: Molecular Descriptorsmentioning

confidence: 99%

“…Studies of this type have great potential, which is used to develop relationships between physicochemical properties of chemical substances and their biological activity. This technique has been successfully applied to drug design and search for new pharmacologically active substances [4][5][6][7][8][9]. The high demand for new pharmacologically active substances, requires alternative experimental methods in order to reduce development time and cost.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks for Predicting Imidazole Derivatives Antimicrobial Activity Against Enterococcus Faecalis

Badura,

Krysiński,

Nowaczyk

et al. 2022

SSRN Journal

View full text Add to dashboard Cite

Application of artificial neural networks to prediction of new substances with antimicrobial activity againstEscherichia coli

Cited by 16 publications

References 22 publications

ABDpred: Prediction of active antimicrobial compounds using supervised machine learning techniques

ABDpred: Prediction of active antimicrobial compounds using supervised machine learning techniques

Application of artificial neural networks for predicting imidazole derivatives antimicrobial activity against Enterococcus faecalis

Application of Artificial Neural Networks for Predicting Imidazole Derivatives Antimicrobial Activity Against Enterococcus Faecalis

Contact Info

Product

Resources

About