Prediction of repurposed drugs for Coronaviruses using artificial intelligence and machine learning

Abstract

The world is facing the COVID-19 pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Likewise, other viruses of the Coronaviridae family were responsible for causing epidemics earlier. To tackle these viruses, there is a lack of approved antiviral drugs. Therefore, we have developed robust computational methods to predict the repurposed drugs using machine learning techniques namely Support Vector Machine, Random Forest, k-Nearest Neighbour, Artificial Neural Network, and Deep Learning. We used the experimentally validated drugs/chemicals with anticorona activity (IC₅₀/EC₅₀) from 'DrugRepV' repository. The unique entries of SARS-CoV-2 (142), SARS (221), MERS (123), and overall Coronaviruses (414) were subdivided into the training/testing and independent validation datasets, followed by the extraction of chemical/structural descriptors and fingerprints (17968). The highly relevant features were filtered using the recursive feature selection algorithm. The selected chemical descriptors were used to develop prediction models with Pearson's correlation coefficients ranging from 0.60 to 0.90 on training/testing. The robustness of the predictive models was further ensured using external independent validation datasets, decoy datasets, applicability domain, and chemical analyses. The developed models were used to predict promising repurposed drug candidates against coronaviruses after scanning the DrugBank. Top predicted molecules for SARS-CoV-2 were further validated by molecular docking against the spike protein complex with ACE receptor. We found potential repurposed drugs namely Verteporfin, Alatrofloxacin, Metergoline, Rescinnamine, Leuprolide, and Telotristat ethyl with high binding affinity. These 'anticorona' computational models would assist in antiviral drug discovery against SARS-CoV-2 and other Coronaviruses.

Keywords: AI; COVID-19; Chemical descriptors; Coronaviruses; Drug repurposing; Machine learning; SARS-CoV-2.

Graphical abstract

Fig. 1

The robustness of the Support Vector Machine models of the Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), and overall Coronavirus was checked using the a) William’s plot between the leverage and the standardized residuals. b) the plot between the actual and predicted pIC50.

Fig. 2

The scatter plot shows the correlation between the actual pIC50 and the predicted pIC50 of the decoy dataset for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), and overall coronaviruses.

Fig. 3

The chemical analysis of the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) inhibitors a) The hierarchical clustering of the SARS-CoV-2 is depicted using the circular plots, b) The 3-dimensional multiscaling plot among the SARS-CoV-2 inhibitors. c) Chemical network showing the status of top-10 predicted repurposed drugs against Coronaviruses (SARS, SARS-CoV-2, and MERS). Blue color of the drug shows the predicted repurposed drugs unique to single virus, green color depicts the common repurposed drugs between SARS-CoV-2 and MERS, orange color shows the common repurposed rugs between SARS and SARS-CoV-2, while the pink color shows the common drug between the SARS and MERS. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 4

The ligands a) Verteporfin, b) Alatrofloxacin, c) Metergoline, d) Rescinnamine, e) Leuprolide, and f) Telotristat ethyl binding the SARS-CoV-2 S-protein. (SARS-CoV-2 S-protein in ribbon diagram with grey color and ligand molecule in green color sphere). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 5

Two-dimensional representation of molecular interactions of a) Verteporfin, b) Alatrofloxacin, c) Metergoline, d) Rescinnamine, e) Leuprolide, and f) Telotristat ethyl with the S-protein of SARS-CoV-2.

Fig. 6

The overall methodology used in the study. The inhibitors of the Coronaviruses (SARS, SARS-CoV-2, and MERS) were extracted from the literature. Splitting of the dataset into the training/testing and independent validation using randomization approach. The descriptors were calculated using PaDel software followed by the selection of relevant features. The prediction model is developed using machine learning algorithms like Support Vector Machine, Random Forest, k-Nearest Neighbor, Artificial Neural Network, and Deep Neural Network.