Artificial Intelligence in Coronavirus Drug and Vaccine Development - Part 2


In Part 1 of the article on AI in COVID Drug and Vaccine development, we discussed about how AI is used extensively in any drug and vaccine development. In this part, we will look deeper into how AI is specifically being used in COVID-19 drug and vaccine development.

The recent applications of Artificial Intelligence for COVID-19 include the virtual screening of both repurposed drug candidates and new chemical entities. For repurposed drugs, the goal has been to rapidly predict and exploit interconnected biological pathways or the off-target biology of existing medicines that are proven safe and can thus be readily tested in new clinical trials. Scientists leveraged AI-derived knowledge graph, which integrates biomedical data from structured and unstructured sources. Similarly, other scientists published an application of their Deep Learning-based drug–target interaction model that predicted commercially available antiviral drugs that may target the COVID-19-related protease and helicase.



On the vaccine front, since the outbreak of this first coronavirus, different AI-based approaches have been used to predict potential epitopes so as to design vaccines. Scientists used MARIA and NetMHCPan4, two supervised neural network-driven tools, to discover potential T-cell epitopes for SARS-CoV-2 close to the 2019-nCoV spike receptor-binding domain (RBD). The other methods used are Long Short-Term Memory (LSTM) network and Recurrent neural networks (RNNs) which have successfully demonstrated the ability to perform when trained on molecules or protein sequences to predict secondary structure, quantitative structure–activity relationship (QSAR) modelling, and function prediction.


Natural language processing models, specifically language modelling techniques, have also made an impact in the domain of COVID-19 vaccine discovery. Pre-trained transformers were used to predict protein interaction and model molecular reactions in carbohydrate chemistry, which can be utilized in the process of vaccine development. As the spike protein of SARS-COV-2 is crucial for viral entry, specific neutralizing antibodies against the receptor-binding domain of Spike can interrupt the attachment and fusion of viral proteins. This method could provide simulated sequences that can serve as a guide for further vaccine discovery against COVID-19 and possibly new zoonosis that may arise in the future.



In summary, Artificial intelligence has been applied to many subfields of drug discovery and vaccine development. This improvement is crucial for the current situation and immediate COVID-19 therapy discovery for several key reasons. Firstly, the automatic feature extraction ability of deep learning can support models with better accuracy and deliver more reliable results. Secondly, the generative ability demonstrated by deep learning models can be utilized to create more druggable molecules and better epitope prediction, lowering the chance of failure in the trial pipeline. Lastly, the novelty of the virus causes the data around its possible therapies to be scarce, which is a suitable scenario for transfer learning and leveraging the learned knowledge from previous tasks.


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