News & Events
News Releases
“Bringing the experience and expertise from three additional DOE national laboratories to ATOM’s current partners,…, reinforces ATOM as a valuable national resource to create powerful new capabilities for the cancer research community, building collaborations and driving advances in translational research to develop treatments more quickly,” said Eric Stahlberg, director of the Biomedical Informatics and Data Science group at FNL and co-lead of the ATOM consortium.
Excelra will give LLNL (a member of the ATOM consortium) full access to GOSTAR – a vast repository of approximately 8 million small molecule discovery compounds and….
“Drug discovery is in the perfect position to take full advantage of AI, with advancements in high-throughout technologies, AI algorithms and high performance computing,” said Kimberly Powell, VP of Healthcare at NVIDIA…
Lawrence Livermore National Laboratory, Frederick National Laboratory for Cancer Research, GSK, and University of California San Francisco will combine vast data stores, supercomputing, and scientific expertise to reinvent discovery process for cancer medicines…
ATOM Blog
To support MoDaC’s objectives, ATOM has released its best model and datasets into MoDaC’s repository for public users to use. The idea is for the user to re-use the machine learning models with ATOM's open-source ATOM Modeling Pipeline (AMPL) software to evaluate small-molecule compounds against selected targets for their drug discovery or chemical toxicology research.
In Michelle Arkin's, Ph.D., Ask the Expert interview with Lab Manager, she describes how ATOM is using Machine Learning and Artificial Intelligence to bring together data from public databases and from pharmaceutical partners to perform multi-parameter optimization on a drug target - coupled with ATOM's pipeline (AMPL) to do automated experiments.
Read more about ATOM’s science from our researchers along with our recent publications! Also, we share updates about our new partners, technical team members, and presentations.
The program aimed to teach the students how to write code, perform machine learning, analyze models, and understand assay development. They built the models using the open-source RuleBender software and AMPL (ATOM Modeling PipeLine) packages. Their models and the abundance of analyzed data advance ATOM’s modeling and molecular design program.
Amanda Paulson of Frederick National Laboratory for Cancer Research won her poster session for the Informatics category at the US Army/NCI Spring Research Festival!
Purdue is answering the call for data science expertise with The Data Mine – a unique program that promotes learning from data and transforming it into important information to help solve real-world problems. The students, living in a community setting, partner on projects with established corporations.
While they comprise about 51% of the general population, women account for only about 17% of the HPC workforce1. Those numbers are slowly improving, thanks to the contributions of numerous female engineers, scientists and researchers.
Join Lawrence Livermore National Laboratory (LLNL) on YouTube to hear Brian Van Essen (led by Sam Ade Jacobs) work on COVID-19 Drug Design, at SuperComputing2020!
This summer, instead of working at a traditional hospital or store pharmacy internship, five Butler University Doctor of Pharmacy students delved into the world of data science and machine learning under the mentorship of experts from the ATOM consortium.
One of the projects I’m involved with has a goal of optimizing preclinical safety predictions so we can incorporate predictive toxicology and computational modelling early in the drug discovery process…
Neha Murad, Ph.D., is a Biomathematician and a Postdoctoral Researcher at ATOM. Neha has a keen interest in the biomedical applications of mathematics and…
One of our goals at ATOM is to optimize preclinical safety predictions in silico, so we can incorporate predictive toxicology early in the drug discovery process…
Amanda Minnich, Ph.D., ATOM Data Scientist, is spearheading the ATOM Data Driven Modeling (DDM) Integrated Project Team, and has led the development of our automated DDM pipeline...
As ATOM gears up to implement its Year-2 goals, Jim Brase, M.S., ATOM co-lead of technology addressed the delegates at the ATOM 1-Year Anniversary event…
At the ATOM 1-Year Anniversary event this past October, over 60 delegates from ATOM member organizations convened at ATOM headquarters. Team leaders shared the major accomplishments…
Stay informed with periodic updates from our Leadership and Technical teams.
News
Akpa’s systems pharmacology models help set ATOM apart from other AI-driven drug discovery methods. She will leverage ORNL’s high-performance computing capabilities, such as the Summit supercomputer, in her quest to get drugs to patients faster with a greater probability of success.
“Tackling a virus requires a multipronged approach in the big-picture way of not only having an arsenal to respond with as the virus changes over time, but also recognizing that trying to kill something that’s not really alive is a very hard task,” says Marti Head of Oak Ridge National Laboratory (ORNL), who leads the molecular design project of the National Virtual Biotechnology Laboratory (NVBL).
A machine learning model developed by a team of Lawrence Livermore National Laboratory (LLNL) scientists to aid in COVID-19 drug discovery efforts is a finalist for the Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research.
After announcing the launch of the COVID-19 High Performance Computing Consortium on Sunday, the Department of Energy yesterday provided more details on its scope and operation in a briefing led by Undersecretary of Energy Paul Dabbar and attended by HPC leaders from national labs. The joint public-private effort will pool 16 systems which together offer…
Lawrence Livermore National Laboratory scientists are contributing to the global fight against COVID-19 by combining artificial intelligence/machine learning, bioinformatics and supercomputing to help discover….
Drug discovery has always been a slow and laborious process by nature, with researchers finding dead ends much more frequently than successful routes to the clinic...
At the world’s pharma and biotech companies, artificial intelligence is increasingly critical to the drug discovery engine…
Argonne has announced its commitment to become the newest member of ATOM, a public-private partnership between national laboratories, academic institutions and private industry…
Since its release just a few months ago, PyTorch 1.0 has been rapidly adopted as a powerful, flexible deep learning platform that enables engineers and researchers to move quickly from research to production…
Radiology. Autonomous vehicles. Supercomputing. The changes sweeping through all these fields are closely related. Just ask NVIDIA CEO Jensen Huang…
A Frederick National Laboratory for Cancer Research-founded organization has announced plans to partner with a California-based technology company to advance its artificial intelligence drug discovery program…
Nvidia is collaborating with medical groups to push GPU-powered AI tools into clinical settings, including radiology and drug discovery…
Nvidia Corp. reckons artificial intelligence has evolved to the point where it can be reliably used to help diagnose diseases and discover new drugs…
The idea of using artificial intelligence (AI) to accelerate drug discovery process and boost a success rate of pharmaceutical research programs has inspired a surge of activity in this area over the last several years…
The recently established Accelerating Therapeutics for Opportunities in Medicine (ATOM) consortium, was conceived by John, with a mission “to accelerate the development of more effective therapies for patients”…
Since its launch in 2016, the Cancer Moonshot initiative has successfully united leading international scientists from industry and academia, whose collaborative efforts have been key to the delivery of more effective tools for cancer diagnosis, treatment and prevention…
Data sharing initiative ATOM, of which GlaxoSmithKline is a member, wants to cut the time it takes to get from concept to molecule through comprehensive data sharing. John Baldoni, founder of ATOM and co-chair of its governing board, talks to Scrip about the consortium’s goals and bringing other big pharmas on board…
Don’t be deceived. John Baldoni may have 37 years’ experience in often conservative pharma. But he's not into incremental innovation…
The average time to identify a new cancer drug is six years. Since one of every four deaths in the U.S. is due to cancer, a lot of lives could be saved if that drug development time could be cut down to just a year…
When I explain my job to my 3-year-daughter, I say that I spend my time going to lots of meetings to listen to people’s ideas, or to ask them to listen to mine. I tell her that once we agree on an idea, it is my job to make it happen…to make it real…
This will give you hope that a treatment or cure is imminent - super computing is joining the search for a cure…
A new open, sharable platform that combines high-powered computing and data shared from public and industry sources is designed to accelerate the discovery of new cancer drugs…
The goal itself is jaw-dropping. Cut the development for cancer-fighting drugs — a notoriously lengthy and expensive process — from six years to one…
A global pharma company is teaming up with federal research labs and academics in an ambitious effort to slash the time spent screening potential cancer drugs before they make it into the clinic for testing…
Two organizations in the San Francisco Bay Area and two outside have come together to cut the time it takes to discover cancer drugs from about six years to one, officials with the consortium said today…
Each year, drugmakers shelve, write off or discard thousands of possible treatments that don’t appear to have value in the clinic…