Replies to post #7866 on AIkido Pharma Inc (AIKI)

[Viewmont]

My pleasure! It's time for them to announce something huge already.
Has been a while we had no big moves.

[Viewmont]

NEW YORK, July 9, 2020 /PRNewswire/ -- AIkido Pharma Inc. (Nasdaq: AIKI) ("AIkido" or the "Company") today provided an update on its effort to identify compounds to potentially address the COVID-19 pandemic. As previously announced, on April 13, 2020, AIkido executed a Master License Agreement with the University of Maryland, Baltimore ("UMB"), covering certain antiviral compounds discovered by UMB.

Recently, the researchers at UMB, including Matthew Frieman, PhD, Associate Professor of Microbiology and Immunology at the University of Maryland School of Medicine (UMSOM), Alexander MacKerell, PhD, Professor of Pharmaceutical Sciences at the University of Maryland School of Pharmacy, and Stuart Weston, PhD, Postdoctoral Fellow at UMSOM, have identified a human protein complex (called the SKI complex) that is important for replication of several viruses including Influenza, COVID-19, SARS1, MERS, SARS2, Ebola and Marburg. Using a computer modeling approach called SILCS from SilcsBio LLC, chemical compounds were identified that are predicted to bind to a pocket on a human protein in this complex. These compounds were tested for their ability to block replication of the Influenza virus in human cells, from which several active compounds were found. These compounds were then tested against the other viruses and found to block replication of these as well. Utilizing the information gleaned from this initial research and the power of the SILCS technology combined with machine learning tools, the researchers are currently identifying compounds with chemical structures similar to the active compounds and testing them to find ones that work better at blocking virus replication.

"This is encouraging progress, demonstrating the importance of machine learning to accelerate pharmaceutical research," commented Anthony Hayes, CEO of AIkido. "Legacy processes, involving manually testing potentially thousands of compounds, are slower and potentially more expensive. Utilizing a combination of a physics-based approach and machine learning, our partners are able to significantly narrow the range of potential compounds to test, accelerating efforts and significantly reducing costs. The ultimate goal is to create a new compound that blocks replication of a wide variety of viruses in humans that will be an effective drug for the treatment of known viral infections as well as those that may arise in the future."