Artificial intelligence, drug repurposing and peer review

Nature Biotechnology. 2020. P. 1127–1131.

Levin J., Oprea T. I., Davidovich S., Clozel T., Overington J., Vanhaelen Q., Cantor C., Bischof E., Zhavoronkov A.

The COVID-19 pandemic has transformed the way scientific and clinical results are shared and disseminated. According to a recent analysis, an average of 367 COVID-19 papers are being published every week, with a median time from submission to acceptance of just 6 days (compared with 84 days for non-COVID-19 content)1. These unprecedented peer review turnaround times — and in some cases relaxed editorial standards — are justifiable in a context where new information may accelerate knowledge and solutions to the emerging global medico-socio-economic disaster, but they also risk the release of preliminary or flawed publications that can mislead research and development efforts, compromise clinical practice and misinform policy makers. What can be done to compensate for inadequate peer review in the context of a pandemic? Here, we propose a strategy whereby rigorous community and peer review is coupled to the use of artificial intelligence to prioritize research and therapeutic alternatives described in the literature, enabling the community to focus resources on treatments that have undergone appropriate and thorough clinical testing.

Keywords: drug repurposing, COVID-19, artificial intelligence