Artificial Intelligence in Coronavirus Drug and Vaccine Development - Part 1
Coronavirus (COVID-19) has
wreaked havoc across the world with more than 263 million infected cases and
close to 5.2 million deaths as of 1st December 2021. The virus, coming from the family of Coronaviridae with
its predecessors Severe Acute Respiratory Disease (SARS) and Middle Eastern
Respiratory Syndrome (MERS), emerging in 2002 and 2013, respectively, has
impacted human population across geographies with USA, Brazil and India
emerging as the leading hotspots with more than 20 million confirmed cases each (USA
has actually topped 48 million). Refer Bloomberg for current update on Mapping theCoronavirus Outbreak Across the World.
UPDATE: The Omicron variant — first identified in South Africa — was designated as a variant of concern by the World Health Organization (WHO) recently. It has become the fifth and latest variant of concern to be categorized as such since the start of the COVID-19 pandemic.
In recent years, machine learning has revolutionized many fields of science and engineering. It has largely transformed our daily lives, from speech and face recognition to customized targeted advertisements. The power of automatic abstract feature learning, combined with a massive volume of data, has immensely contributed to the successful application of ML. Two of the most impactful areas affected in the medical world are drug and vaccine discovery, in which ML has offered compound property prediction, activity prediction, reaction prediction, and ligand–protein interaction.
While hospitals and laboratories
worldwide are resorting to trial and error tactics for COVID-19 drug discovery
and vaccine development, Virtual Screening (VS) has emerged as a popular method
for discovering potent compounds due to the inefficiency of lab-based high
throughput screening (HTS). VS for rational drug discovery is essentially an
approach that involves computationally targeting a specific biomolecule (e.g.,
DNA, protein, RNA, lipid) of a cell to inhibit its growth and/or activation. Additionally,
conventional vaccine discovery methods have been costly, and it may take many
years to develop an appropriate vaccine against a specified pathogen.
Over the past decade, artificial
intelligence (AI)-based models have revolutionized drug discovery in general. AI
has also led to the creation of many Reverse Vaccinology (RV) virtual
frameworks, which are generally classified as rule-based filtering models. Machine
learning (ML) enables the creation of models that learn and generalize the
patterns within the available data and can make inferences from previously
unseen data. With the advent of deep learning (DL), the learning procedure can
also include automatic feature extraction from raw data. Moreover, it has
recently been found that deep learning's feature extraction can result in
superior performance compared to other computer-aided models.
Machine learning has also improved the field of
vaccine design over the past two decades. VaxiJen was the first implementation
of ML in RV approaches and has shown promising results for antigen prediction. In
addition, the recent development of Vaxign-ML, a web-based RV program
leveraging machine learning approaches for bacterial antigen prediction, is a
testament to the success of exercising mathematical ML-based in RV. In essence,
these pipelines consist of feature extraction, feature selection, data
augmentation, and cross-validation implemented to predict vaccine candidates
against various bacterial and viral pathogens known to cause infectious