Faculty, Staff and Student Publications
Publication Date
7-1-2023
Journal
Heliyon
Abstract
The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of
Keywords
Artificial intelligence, Biotechnology, Graph neural networks, Molecule representation, Reinforcement learning, Drug discovery, Molecular dynamics simulation
DOI
10.1016/j.heliyon.2023.e17575
PMID
37396052
PMCID
PMC10302550
PubMedCentral® Posted Date
6-26-2023
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Genetic Phenomena Commons, Medical Genetics Commons, Oncology Commons