SAN FRANCISCO, April 19, 2017 /PRNewswire/ -- Atomwise Inc. seeks proposals from innovative university scientists to receive 72 potential medicines, generated specifically for their research by artificial intelligence. The Artificial Intelligence Molecular Screen (AIMS) program is designed to dramatically accelerate the race towards life-saving drugs by analyzing millions of compounds for each disease. Contributing cutting-edge AI technology and delivering physical molecules to as many as 100 labs, the program is the first of its kind.
"It's this easy: researchers tell us the disease and protein to target, we screen millions of molecules for them, and then they receive 72 custom-chosen compounds ready for testing," said Dr. Han Lim, MD, PhD, Atomwise's Academic Partnerships Executive. "As a former UC Berkeley principal investigator, I helped design the kind of program I wish existed for my own work."
AIMS is a streamlined program. Short applications are submitted online, and recipients will be announced three months from the submission deadline. No preliminary data is required and projects showing success can receive further support.
Atomwise develops artificial intelligence systems for drug discovery. Its groundbreaking AtomNet technology reasons like a human medicinal chemist, using powerful Deep Learning algorithms and supercomputers to analyze millions of potential treatments each day.
Historically, discovering a single new medicine cost billions of dollars and took an average of 15 years – putting such research outside the reach of academic scientists. Atomwise helps predict the effectiveness of new drugs more rapidly, much like software used to simulate aircraft, buildings, and computer chips.
"Technology this sophisticated is often regarded as the province of big companies, who use large-scale Deep Learning to power computer assistants and self-driving cars," said Alexander Levy, Co-Founder and Chief Operating Officer of Atomwise. "We're putting the best of Silicon Valley artificial intelligence in the hands of university researchers, for free."
Atomwise has already launched 27 drug discovery projects with leading research institutions. These partnerships are advancing research on diseases as diverse as Ebola, multiple sclerosis, and leukemia. Molecules predicted by Atomwise have become lead medicinal chemistry candidates and successfully treated animals in trials.
"Global health crises are mounting and universities are facing cutbacks in research funding," added Mr. Levy. "Initiatives like AIMS are stepping in to help ensure we don't lose the next generation of medical breakthroughs."
"This is a unique opportunity to conduct transformative research with an industrial partner at no cost," concluded Dr. Lim. "I encourage every academic scientist in search of new chemical and biological discoveries to apply."
Atomwise Academic Awards Program Details:
- Request for proposals issued: April 19, 2017
- Request for proposals deadline: 11:59 PM PDT, June 12, 2017
- Announcement of recipients: September 2017
- Eligibility and Requirements: The application must be supported by a principal investigator, and the research must be performed within the United States or Canada at a non-profit university or research institute.
Atomwise uses artificial intelligence to help discover new medicines. Its groundbreaking AtomNet technology reasons like a human medicinal chemist, using powerful Deep Learning algorithms and supercomputers to analyze millions of potential treatments each day. Atomwise has launched 27 drug discovery projects with innovative organizations, including Merck and Harvard, advancing research in diseases as diverse as Ebola, multiple sclerosis, and leukemia. Now, through its many partnering programs, Atomwise is actively seeking new drug discovery collaborators to address the health challenges of our time with the power of artificial intelligence.
To view the original version on PR Newswire, visit:http://www.prnewswire.com/news-releases/atomwise-opens-applications-for-historic-ai-drug-discovery-awards-300441615.html