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Cooling down computers + AI speeds up discovery & production of anti-viral drugs

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A new system for cooling down computers could revolutionize the pace of innovation
https://www.smithsonianmag.com/science-n...180975807/

EXCERPT: As electricity meets resistance passing through those processors it creates heat. More processors mean higher temperatures, threatening the continued growth of computer power because as they get hotter, chips decrease in efficiency and eventually fail. There’s also an environmental cost. Those chips, and the cooling they require, devour power with an insatiable hunger. Data centers use roughly one percent of the world's electricity. In the United States alone, they consume electricity and water for cooling roughly equivalent to that used by the entire city of Philadelphia in a year.

Now, Swiss researchers have published a study in the journal Nature which says they have one solution to the cooling problem. “Data centers consume a huge amount of electricity and water as as we rely more and more on this data, this consumption is just going to increase,” says Elison Matioli, a professor in the Institute of Electrical Engineering at Ecole Polytechnique Fédérale de Lausanne (EPFL) who led the study. “So finding ways to deal with the dissipated heat or dissipated power is an extremely important issue.”

Previous attempts to cool microchips have relied on metal sinks, often combined with fans, that absorb heat and act like an exhaust system. Some data centers rely on fluid flowing through servers to draw away heat. But those systems are designed and fabricated separately and then combined with the chips. Matioli and his team have designed and fabricated chips and their fluid cooling systems together. In the new design, the cooling elements are integrated throughout by creating microchannels for fluid within semiconductors that spirit away the heat, save energy, and mitigate the environmental problems created by data centers.

Their work also could have important applications in an electrified future, helping eliminate the heat problem and reducing the size of power converters on cars, solar panels and other electronics. “The proposed technology should enable further miniaturization of electronics, potentially extending Moore's Law and greatly reducing the energy consumption in cooling of electronics,” they write... (MORE - details)


Can AI and automation deliver a COVID-19 antiviral while it still matters?
https://spectrum.ieee.org/artificial-int...ll-matters

EXCERPTS: . . . just two of hundreds of biotech firms eager to overhaul the drug-discovery process, often with the aid of artificial intelligence (AI) tools. The first set of antiviral drugs to treat COVID-19 will likely come from sifting through existing drugs.

[...] Traditional drug discovery is far too slow to react to a pandemic. Designing a drug from scratch typically takes three to five years—and that’s before human clinical trials. “Our goal, with the combination of AI and automation, is to reduce that down to six months or less,” says Collins, who is chief strategy officer at SRI Biosciences, a division of the Silicon Valley research nonprofit SRI International. “We want to get this to be very, very fast.”

That sentiment is shared by small biotech firms and big pharmaceutical companies alike, many of which are now ramping up automated technologies backed by supercomputing power to predict, design, and test new antivirals—for this pandemic as well as the next—with unprecedented speed and scope. “The entire industry is embracing these tools,” says Kara Carter, president of [...] a drug-discovery company in Hamburg. “Not only do we need [new antivirals] to treat the SARS-CoV-2 infection in the population, which is probably here to stay, but we’ll also need them to treat future agents that arrive.”

[...] Iktos’s AI platform was created by a medicinal chemist and an AI expert. To tackle SARS-CoV-2, the company used generative models—deep-learning algorithms that generate new data—to “imagine” molecular structures with a good chance of disabling a key coronavirus protein.

For a new drug target, the software proposes and evaluates roughly 1 million compounds, says Gaston-Mathé. It’s an iterative process: At each step, the system generates 100 virtual compounds, which are tested in silico with predictive models to see how closely they meet the objectives. The test results are then used to design the next batch of compounds. “It’s like we have a very, very fast chemist who is designing compounds, testing compounds, getting back the data, then designing another batch of compounds,” he says.

The computer isn’t as smart as a human chemist, Gaston-Mathé notes, but it’s much faster, so it can explore far more of what people in the field call “chemical space”—the set of all possible organic compounds. Unexplored chemical space is huge: Biochemists estimate that there are at least 1063 possible druglike molecules, and that 99.9 percent of all possible small molecules or compounds have never been synthesized.

Still, designing a chemical compound isn’t the hardest part of creating a new drug. After a drug candidate is designed, it must be synthesized, and the highly manual process for synthesizing a new chemical hasn’t changed much in 200 years. It can take days to plan a synthesis process and then months to years to optimize it for manufacture.

That’s why Gaston-Mathé was eager to send Iktos’s AI-generated designs to Collins’s team at SRI Biosciences. With $13.8 million from the Defense Advanced Research Projects Agency, SRI Biosciences spent the last four years automating the synthesis process. The company’s automated suite of three technologies, called SynFini, can produce new chemical compounds in just hours or days, says Collins.... (MORE - details)
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