The #WeAreHEWMEN citizen science/crowdsourcing effort is intended to encourage the video game community to donate spare computer processing power to find treatments to fight the COVID-19 virus. To make it possible, BALANCED has launched the HEWMEN Cell application , a free secure download for 64-bit Windows 10 PCs that creates a virtual network to process drug discovery data for COVID-1, while Complexity is urging the gaming community to support the effort by downloading the HEWMEN app.
"If there's one universal truth about gamers, it's that they're always looking for their next challenge," says Robert M. Atkins, BALANCED CEO. "COVID-19 is the biggest threat to humanity and there's no community in the world more engaged online than gamers. This makes them the perfect group to empower to help put an end to the COVID-19 pandemic. When they hear the call to help, they will answer. It's the moment they've been training for since they booted up their first video game."
The company's HEWMEN technology creates a distributed network that processes data-driven problems such as drug discovery and medical research. The HEWMEN Cell application, which uses small amounts of volunteer computers' unused processing capacity, has integrated BOINC, an open-source "compute for science" software platform used for volunteer resources developed by the University of California, Berkeley that allows for the virtualizing of servers and applications inside a voluntary grid network.
The technology, says the company, has already been widely used with no security issues on millions of PCs. The HEWMEN Cell app was previously used by BALANCED and computational biologist John Wise, Ph.D., of Southern Methodist University's Drug Discovery Lab, for finding co-medications to enhance the effect of chemotherapy in the treatment of recurrent, resistant breast and prostate cancers.
BALANCED, with John Wise, will harness the power of gamers to have the HEWMEN Cell app process information from more than 200,000 FDA-approved existing medications/compounds against models of the protein and enzymatic functions