Artificial Intelligence in Coronavirus Drug and Vaccine Development - Part 1
Coronavirus (COVID-19) has wreaked havoc across the world with more than 22 million infected cases and close to 800 thousand deaths as of 20th August 2020. 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 10 million confirmed cases each (USA has actually topped 27 million). Refer Bloomberg for current update on Mapping theCoronavirus Outbreak Across the World.
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