Why and how should we use Artificial Intelligence, Machine Learning, and Deep Learning Approaches Differently on COVID-19 Coronavirus and Other Pathogens Research?

Authors: Cheng JTJ*

DOI: 10.23880/aii-16000138

Volume 3, Issue 1

Abstract

Artificial intelligence (AI), machine learning (ML), and deep learning (DL) have become increasingly popular tools and research methodology in many scientific research fields. Examples include improving prediction models by integrating mechanistic immunological information into machine learning [1], using multiomics and spatial integration approaches in conjunction with AI and ML methods to guide future informed cell engineering and precision medicine based on immunological studies data [2], and various other studies summarized by Jabbari P, et al. [3]. Using AI, ML, and DL as novel methods to gain new insights to generate novel vaccine and/or drug designs and discovery has been a revolutionary approach over the past decades [4]. Traditionally, researchers resolve to other computational methods (e.g., molecular dynamics (MD) simulation) to help solve problems of and arise from inadequate and/ unsatisfactory drug binding and affinity to target site(s).

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