Artificial intelligence in COVID-19 drug repurposing

Abstract

Drug repurposing or repositioning is a technique whereby existing drugs are used to treat emerging and challenging diseases, including COVID-19. Drug repurposing has become a promising approach because of the opportunity for reduced development timelines and overall costs. In the big data era, artificial intelligence (AI) and network medicine offer cutting-edge application of information science to defining disease, medicine, therapeutics, and identifying targets with the least error. In this Review, we introduce guidelines on how to use AI for accelerating drug repurposing or repositioning, for which AI approaches are not just formidable but are also necessary. We discuss how to use AI models in precision medicine, and as an example, how AI models can accelerate COVID-19 drug repurposing. Rapidly developing, powerful, and innovative AI and network medicine technologies can expedite therapeutic development. This Review provides a strong rationale for using AI-based assistive tools for drug repurposing medications for human disease, including during the COVID-19 pandemic.

Figure 1

Overview of AI-assisted drug repurposing for COVID-19 AI algorithms can be used for drug repurposing, which is a rapid and cost-effective way to discover new therapy options for emerging diseases. Reproduced by permission of Cleveland Clinic Center for Medical Art and Photography. AI=artificial intelligence. PARP1=poly-ADP-ribose polymerase 1. NR3C1=nuclear receptor subfamily 3 group C member 1. AAK1=AP2-associated protein kinase 1. MTNR1A=melatonin receptor 1A. TMPRSS2=transmembrane serine protease 2. ACE2=angiotensin I converting enzyme 2. NRP1=neuropilin 1. NSP14=non-structural protein 14.

Figure 2

AI for drug repurposing in an integrative context AI approaches can greatly accelerate drug repurposing by incorporating biological knowledge (eg, human interactome, organelles, tissues, and organs). The cogs indicate computer programs and algorithms. Red and black circles represent neurons in deep neural networks. Red indicates that this neuron carries important information from the biological systems. Green and blue people indicate different subgroups that might have different responses to the treatment. The downward arrows show that AI algorithms can use the information from multi-level biological systems and drug development pipelines to build more powerful models. The left panel shows the biological systems and the right panel shows the drug development pipeline AI=artificial intelligence.

Figure 3

AI for patient stratification and personalised treatment AI approaches can accelerate precision medicine by the unique integration of genomic, transcriptomic, proteomic, and phenomic profiles from individuals. The bottom left panel represents the feature extraction step using deep learning, the arrows represent successive feature extraction from previous layers, and the shades of grey represent the output (learned features that have high or low values in the output of learned features from previous layers). AI=artificial intelligence.