A computational approach to assist healthcare professionals in selecting antibacterial drugs to treat bacterial infections

Abstract

The reproducibility of experiments has been a long-standing obstruction for farther scientific evolution. Computational methods are being involved to accelerate and to economize drug discovery and the development process. In this work several computational models using matrix completion techniques including matrix factorization, deep matrix factorization, binary matrix completion and graph regularised techniques (graph regularised deep matrix factorisation, graph regularised matrix factorization, graph regularised binary matrix completion and graph regularised matrix completion) have been proposed to predict bacteria-drug association. Here drug-bacteria association matrix is formed. Along with it we gather similarity information using the chemical structure of drugs and genome-genome distance calculator Meier-Kolthoff et al. (2022) for bacteria. Using several matrix completion tools, the bacteria-drug association data and similarity data, the present study predicts the set of best possible drugs corresponding to each bacteria in the database. The graph regularised techniques consider the drugbacteria association matrix along with the similarity information for prediction. To evaluate robustness of the model, cross validation settings on different scenarios have been adopted on the training data. The AUC-AUPR metric is being reported corresponding these scenarios and association between drug-bacteria is being predicted with the help of various graph and non graph regularised methods. The result produced by graph regularised methods are better compared to non graph regularised methods. Hence it can be concluded that the graph regularised methods predicts the association data well. We anticipate that this work will provide opportunities to develop drugs for newly discovered bacteria and, conversely, enable the identification of potential bacteria targets for existing drugs