Multi-scale temporal convolutional networks and continual learning based in silico discovery of alternative antibiotics to combat multi-drug resistance

Abstract

The high incidence of diseases caused by multi-drug resistant (MDR) pathogens combined with the shortage of effective antibiotics has necessitated the development of in-silico machine and deep learning tools to facilitate rapid drug discovery. The construction of computational models to discover antibacterial peptides (ABPs) in proteins of various organisms to develop a new line of antibiotics has emerged as a possible recourse. To this end, we used multi-scale temporal convolutional networks (MSTCN) to develop a robust deep learning-based model called MSTCN-ABPpred (BL) that classifies ABPs with an accuracy of 98% (which is better than various state-of-the-art models). The main contribution of this proposed work is that we have incorporated a continual learning module in this model so that it keeps adapting itself dynamically by re-training on new data points. This re-trainable version of the baseline model (MSTCN-ABPpred (BL)) was termed as MSTCN-ABPpred (CL). We re-trained this model on the ABPs and non-ABPs predicted by it in some antibacterial proteins. It has been demonstrated that the proposed model does not exhibit any statistically significant deterioration in performance after extensive re-training, and it gains additional skills compared to the MSTCN-ABPpred (BL). We have also deployed a freely accessible web application based on our final model, available at https://mstcn-abppred.anvil.app/, which can identify and discover ABPs in a protein using which the model gets re-trained on its own.