Artificial intelligence and machine learning for protein toxicity prediction using proteomics data

Instead of only focusing on the targeted drug delivery system, researchers have a great interest in developing peptide‐based therapies for the procurement of numerous class of diseases. The main idea behind this is to anchor the properties of the receptor to design peptide‐based therapeutics. As these macromolecules have distinct physicochemical properties over small molecules, it becomes an obligatory field for the treatment of diseases. For this, various in silico models have been developed to speculate the proteins by virtue of the application of machine learning and artificial intelligence. By analysing the properties and structural alert of toxic proteins, researchers aim to dissert some of the mechanisms of protein toxicity from which therapeutic insights may be drawn. Numerous models already exist worldwide emphasizing themselves as leading paramount for toxicity prediction in protein macromolecules. Few of them comparatively compete with the other predictive protein toxicity models and convincingly give a high‐performance result in terms of accuracy. But their foundation is quite ambiguous, and varying approaches are found at the level of toxicoproteomic data utilization while building a machine learning model. In this review work, we present the contribution of artificial intelligence and machine learning approaches in prediction of protein toxicity using proteomics data. Here, we present the contribution of artificial intelligence and machine learning approaches in prediction of protein toxicity using proteomics data. Several diseases are evident to relate with protein toxicity as one of the main pathogenic mechanism, the details of which are still not fully understood. This review study aims to explore the different aspects of artificial intelligence/machine learning‐based predictive protein toxicity modelling inclusively toxic proteomics data collation and processing.