Use of artificial intelligence in vaccine development against pathogens: Challenges and future directions

Abstract

Introduction Owing to the high prevalence of viral diseases, and emerging drug resistance in microorganisms, particularly in parasites and bacteria have sparked the development of a novel vaccine candidate using artificial intelligence (AI – a term given by McCarthy in 1956)[1] and this provides an alternative to the traditional approaches because it takes years to generate an effective vaccine since conventional approaches are considerably costly and time-consuming. In an effort to target an important endemic infection, international health organizations are also speeding up their vaccine research program.[2] Substantial information, i.e., proteomic and genomic data have been developed in the research and medical fields by recent advances in high throughput experimental processes and such data allow AI to provide incredibly accurate forecasts and predictions. Adding to this, by 2025, it is predicted that the pharma industry will reach up to $45.2 billion.[3] Nevertheless, population genetic diversity, new recombination with high mutation rates in viral replication, antigenic variations, species specificity of antigens (Ags), also development, affordability, acceptability, accessibility, and the 5C model of vaccine hesitancy cannot be disregarded. This cutting-edge, deep learning, and genetic algorithms technology, i.e., AI when paired with tried-and-true laboratory methods might hasten the creation of vaccinations against infectious diseases. AI methods such as deep neural networks, artificial neural networks, and gradient-boosting decision trees are leveraged to predict and detect the target epitopes-portions that activate the immune system thus, assisting in the preparation of a vaccine library key. AI is being effectively employed in the analysis of immunological data, immune response prediction for vaccine efficacy/disease progression, discovery of biomarkers, and large-scale immunological dataset analysis. This would lessen the time duration for target identification, streamline the development of future vaccine candidates, and expedite the creation of potential future vaccines. More elaborately AI is the newest and most complex scientifically developed technique which is the broad discipline of computer science. Computational understanding of machines is made possible by integrating biological and cognitive data sets to construct inventive machine learning (ML). Informatively and not to be overlooked there is the conceptual distinction between AI and ML, despite their close relationship as both are supervised learning. AI is meant to build devices or systems that are capable of carrying out activities that call for human intellect whereas ML is a subset of AI and is employed in the larger subject of AI to produce pronounced output. In general, the vaccine candidate encompasses blueprints of the pathogens with other components to ensure efficacy and safety[4] and AI helps to identify the lead Ag that holds the potential to promptly trigger the immunity. Coalition for Epidemic Preparedness Innovations outlined to accelerate vaccine development employing AI and establishment of a vaccine library.[5] Throughout the years, AI has been firmly integrated with reverse vaccinology (RV) which is a genome-based vaccine design technology[6] and uses bioinformatics to examine the pathogen genome and identify a good candidate for vaccination.[7] For instance, VaxiJen is one of the earliest AI-driven prediction techniques for Ag identification, based on the characteristic feature of amino acid residues to exhibit antigenicity.[8] Using protein sequences’ biological and physicochemical characteristics as input variables, Vaxign-ML trained five distinct machine learning models [Table 1]. The database Protegen which has been gathering and cataloguing protective Ags that have been empirically verified for the past 10 years, provided the input protein sequences.[9] Following are certain illustrations in different sections that how current