# ***Email Competition Organizers***

If you haven't done so already, send an email to the organizing team (Bobak Mortazavi (bobakm@tamu.edu), Ryan King (kingrc15@tamu.edu), Subhamoy Mandal (s.mandal@ieee.org) and Tayo Obafemi-Ajayi (tayoobafemiajayi@missouristate.edu)) for information about the next step.

# Challenge Description:

Tens of thousands of research papers on the SARS-CoV-2 virus and the COVID-19 illness have flooded journals and preprint servers, with many more added every day. This publication rate is leaving researchers unable to keep up with new findings and insights, even as they rely on these to inform their own work in understanding and combating the disease. In particular, research groups investigating novel drug candidates with in-silico drug discovery efforts are in essence searching for a proverbial needle in a haystack, and so rely on scientific insights from the literature to guide their efforts toward the most fruitful directions. Thus, they are left to navigate the current publication deluge for their primary guidance. In order to keep pace with the vast amount of relevant literature that continues to grow, researchers need more capable tools to locate and filter the information they seek in these publications. To meet this pressing need, it is desirable to develop and apply the latest Natural Language Processing (NLP) techniques for efficiently locating and filtering relevant information from a growing dataset of COVID-related literature, to help accelerate drug discovery research. The proposed NLP challenge task is framed towards designing and building a Question-Answering (QA) system to find answers to COVID-related questions in the scientific literature.

# Goal:

A practical usage of NLP models with COVID relevant papers might be automated information extraction from literature to facilitate drug discovery efforts. One of the crucial elements that can inform these efforts is the knowledge about viral proteins. The goal of this data challenge to build an NLP model to identify answers to protein-related questions from scientific papers.