In its on-going campaign to reveal the inner workings of the SARS-CoV-2 virus, the U.S. Department of Energy's (DOE) Argonne National Laboratory is leading efforts to couple artificial intelligence (AI) and cutting-edge simulation workflows to better understand biological observations and accelerate drug discovery. Argonne collaborated with academic and commercial research partners to achieve near real-time feedback between simulation and AI approaches to understand how two proteins in the SARS-CoV-2 viral genome, nsp10 and nsp16, interact to help the virus replicate and elude the host's immune system. The team achieved this milestone by coupling two distinct hardware platforms: Cerebras CS-1, a processor-packed silicon wafer deep learning accelerator; and ThetaGPU, an AI- and simulation-enabled extension of the Theta supercomputer, housed at the Argonne Leadership Computing Facility, a DOE Office of Science User Facility. To enable this capability, the team developed Stream-AI-MD, a novel application of the AI method called deep learning to drive adaptive molecular dynamics (MD) simulations in a streaming manner. Data from simulations is streamed from ThetaGPU onto the Cerebras CS-1 platform to simultaneously analyze how the two proteins interact.

The existential threat of Covid-19 has highlighted an acute need to develop working therapeutics against emerging health concerns. One of the luxuries deep learning has afforded us is the ability to modify the landscape as it unfolds -- so long as we can keep up with the viral threat, and access the right data. As with all new medical maladies, oftentimes the data need time to catch up, and the virus takes no time to slow down, posing a difficult challenge as it can quickly mutate and become resistant to existing drugs. This led scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Jameel Clinic for Machine Learning in Health to ask: How can we identify the right synergistic drug combinations for the rapidly spreading SARS-CoV-2? Typically, data scientists use deep learning to pick out drug combinations with large existing datasets for things like cancer and cardiovascular disease, but, understandably, they can't be used for new illnesses with limited data.

The existential threat of COVID-19 has highlighted an acute need to develop working therapeutics against emerging health threats. One of the luxuries deep learning has afforded us is the ability to modify the landscape as it unfolds -- so long as we can keep up with the viral threat, and access the right data. As with all new medical maladies, oftentimes the data needs time to catch up, and the virus takes no time to slow down, posing a difficult challenge as it can quickly mutate and become resistant to existing drugs. This led scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) to ask: how can we identify the right synergistic drug combinations for the rapidly spreading SARS-CoV-2? Typically, data scientists use deep learning to pick out drug combinations with large existing datasets for things like cancer and cardiovascular disease, but, understandably, they can't be used for new illnesses with limited data.

Of course, this is not the first global epidemic and it will not be the last. Humans have experienced four global epidemics throughout history, if they are Black Death (14th Century), Spanish influenza (1918), The global epidemics of HIV / AIDS (20th century), and SARS (2002–2003) have affected many parts of the world. Covid-19 stands out because of such high international tours. It spread rapidly around the world at an unexpected time and as a result, many countries have completely shut it down. At the time of writing(07/06/2021), more than 173 million Covid-19 patients have been reported worldwide and more than 3.7 million have died. Covid-19 is also different from previous epidemics.

The world is currently facing an unprecedented health crisis. The coronavirus known as COVID-19 has already taken the lives of thousands of people worldwide and has infected many more. At the time of writing, the crisis is not over yet, and there are concerns that it might lead into a new financial crisis. At these times of crisis, humanity needs any weapon at its disposal. So, what can AI and data science do in order to help with this crisis?

Drone Defence Services and the University of Nottingham will develop sensor technology to track aircraft. By monitoring all aircraft, Drone Defence aims to prevent drone misuse and enable drones to safely share the sky with other aircraft.

If umifenovir, a broad-spectrum antiviral, can fight COVID-19, then computer-designed synthetic routes could make it easy and cheap to produce. Science's COVID-19 reporting is supported by the Pulitzer Center and the Heising-Simons Foundation. As scientists uncover drugs that can treat coronavirus infections, demand will almost certainly outstrip supplies--as is already happening with the antiviral remdesivir. To prevent shortages, researchers have come up with a new way to design synthetic routes to drugs now being tested in some COVID-19 clinical trials, using artificial intelligence (AI) software. The AI-planned new recipes--for 11 medicines so far--could help manufacturers produce medications whose syntheses are tightly held trade secrets.

Each day from within our homes, we see COVID-19 dominating news cycles as it upends lives, economies, and systems across the globe. Our shared humanity--and the need for empathy--is emphasized by this pandemic, but without the option to have human interaction, what do we do? From working to find a cure to finding ways to stop the spread to helping organizations help others, artificial intelligence (AI) companies are providing innovative ways to keep the world running and experiences connected. Robotics have been finding their way into businesses over the last decade, but now they are used in the front lines of the pandemic. A "smart field hospital" was built in the Hongshan Sports Center in Wuhan, China, following the start of the outbreak.

Ahmed Elnaggar and Michael Heinzinger are helping computers read proteins as easily as you read this sentence. The researchers are applying the latest AI models used to understand text to the field of bioinformatics. Their work could accelerate efforts to characterize living organisms like the coronavirus. By the end of the year, they aim to launch a website where researchers can plug in a string of amino acids that describe a protein. Within seconds, it will provide some details of the protein's 3D structure, a key to knowing how to treat it with a drug.

However, as with machine learning, the team needs people to literally "submit" the sounds of their coughs.