Generative AI develops potential new drugs for antibiotic-resistant bacteria
With nearly 5 million deaths linked to antibiotic resistance globally every year, new ways to combat resistant bacterial strains are urgently needed.
Mar 28, 2024
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With nearly 5 million deaths linked to antibiotic resistance globally every year, new ways to combat resistant bacterial strains are urgently needed.
Mar 28, 2024
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Combinatorial optimization problems (COPs) have applications in many different fields such as logistics, supply chain management, machine learning, material design and drug discovery, among others, for finding the optimal ...
Mar 25, 2024
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In our current age of artificial intelligence, computers can generate their own "art" by way of diffusion models, iteratively adding structure to a noisy initial state until a clear image or video emerges.
Mar 21, 2024
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We are tasking our computers with processing ever-increasing amounts of data to speed up drug discovery, improve weather and climate predictions, train artificial intelligence, and much more. To keep up with this demand, ...
Jan 22, 2024
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A new, potentially revolutionary artificial intelligence framework called "Blackout Diffusion" generates images from a completely empty picture, meaning that, unlike other generative diffusion models, the machine-learning ...
Jan 11, 2024
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Which drug molecule is most effective? Researchers are feverishly searching for efficient active substances to combat diseases. These compounds often dock onto proteins, which usually are enzymes or receptors that trigger ...
Nov 13, 2023
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The entirety of the known universe is teeming with an infinite number of molecules. But what fraction of these molecules have potential drug-like traits that can be used to develop life-saving drug treatments? Millions? Billions? ...
Jul 12, 2022
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Predicting molecular properties quickly and accurately is important to advancing scientific discovery and application in areas ranging from materials science to pharmaceuticals. Because experiments and simulations to explore ...
Mar 4, 2022
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In its on-going campaign to reveal the inner workings of the Sar-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 ...
Sep 1, 2021
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A drug molecule developed though machine learning? An announcement has been made that a phase I clinical study of DSP-1181, that was created using Artificial Intelligence (AI), has been initiated in Japan.
In the fields of medicine, biotechnology and pharmacology, drug discovery is the process by which drugs are discovered and/or designed.
In the past most drugs have been discovered either by identifying the active ingredient from traditional remedies or by serendipitous discovery. A new approach has been to understand how disease and infection are controlled at the molecular and physiological level and to target specific entities based on this knowledge.
The process of drug discovery involves the identification of candidates, synthesis, characterization, screening, and assays for therapeutic efficacy. Once a compound has shown its value in these tests, it will begin the process of drug development prior to clinical trials.
Despite advances in technology and understanding of biological systems, drug discovery is still a lengthy, "expensive, difficult, and inefficient process" with low rate of new therapeutic discovery. Information on the human genome, its sequence and what it encodes has been hailed as a potential windfall for drug discovery, promising to virtually eliminate the bottleneck in therapeutic targets that has been one limiting factor on the rate of therapeutic discovery.[citation needed] However, data indicates that "new targets" as opposed to "established targets" are more prone to drug discovery project failure in general[citation needed] This data corroborates some thinking underlying a pharmaceutical industry trend beginning at the turn of the twenty-first century and continuing today which finds more risk aversion in target selection among multi-national pharmaceutical companies.[citation needed]
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