ABSTRACT
In this study, we generated a Digital Twin for SARS-CoV-2 by integrating data and meta-data with multiple data types and processing strategies, including machine learning, natural language processing, protein structural modeling, and protein sequence language modeling. This approach enabled the computational design of broadly neutralizing antibodies against over 1300 different historical strains of SARS-COV-2 containing 64 mutations in the receptor binding domain (RBD) region. The AI-designed antibodies were experimentally validated in real-virus neutralization assays against multiple strains including the newer Omicron strains that were not included in the initial design base. Many of these antibodies demonstrate strong binding capability in ELISA assays against the RBD of multiple strains. These results could help shape future therapeutic design for existing strains, as well as predicting hidden patterns in viral evolution that can be learned by AI for developing future antiviral treatments.
ABSTRACT
The novel coronavirus (SARS-CoV-2) which causes COVID-19 is an ongoing pandemic. There are ongoing studies with up to hundreds of publications uploaded to databases daily. We are exploring the use-case of artificial intelligence and natural language processing in order to efficiently sort through these publications. We demonstrate that clinical trial information, preclinical studies, and a general topic model can be used as text mining data intelligence tools for scientists all over the world to use as a resource for their own research. To evaluate our method, several metrics are used to measure the information extraction and clustering results. In addition, we demonstrate that our workflow not only have a use-case for COVID-19, but for other disease areas as well. Overall, our system aims to allow scientists to more efficiently research coronavirus. Our automatically updating modules are available on our information portal at https://ghddi-ailab.github.io/Targeting2019-nCoV/ for public viewing.