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Suggestions for second-pass anti-COVID-19 drugs based on the Artificial Intelligence measures of molecular similarity, shape and pharmacophore distribution.

preprint
revised on 15.04.2020 and posted on 17.04.2020 by Martyna Moskal, Wiktor Beker, Rafał Roszak, Ewa P. Gajewska, Agnieszka Wołos, Karol Molga, Sara Szymkuć, Grzegorz Grynkiewicz, Bartosz Grzybowski
Artificial Intelligence algorithms are used to identify “progeny” drugs that are similar to the “parents” already being tested against COVID-19. These algorithms assess similarity not only by the molecular make-up of the molecules, but also by the “context” in which specific functional groups are arrangedand/or by three-dimensional distribution of pharmacophores. The parent-progeny relationships span same-indication drugs (mostly antivirals) as well as those in which the “progenies” have different and perhaps less intuitive primary indications (e.g., immunosuppressant or anti-cancer progenies from antiviral parents). The “progenies” are either already approved drugs or medications in advanced clinical trials – should the currently tested “parent” medicines fail in clinical trials, these “progenies” could be, therefore, re-purposed against the COVID-19 on the timescales relevant to the current pandemic.

Funding

IBS-R020- D1

B634874

Allchemy

History

Email Address of Submitting Author

nanogrzybowski@gmail.com

Institution

Allchemy, Inc.

Country

USA

ORCID For Submitting Author

0000-0001-6613-4261

Declaration of Conflict of Interest

The authors are owners and/or contractors of Allchemy, Inc. This being said, the authors declare no financial interest in the current work.

Licence

Exports