Revival Update Scientific Developments Supporting Revival Technologies Reported by R. Michael Perry, Ph.D.
A Deep Learning Approach to Antibiotic Discovery
disease-causing bacteria, including some strains that are resistant to all known antibiotics. It also cleared infections in two different mouse models.
Jonathan M. Stokes, Kevin Yang, Kyle Swanson, Wengong Jin, Andres Cubillos-Ruiz, Nina M. Donghia, Craig R. MacNair, Shawn French, Lindsey A. Carfrae, Zohar Bloom-Ackerman, Victoria M. Tran, Anush Chiappino-Pepe, Ahmed H. Badran, Ian W. Andrews, Emma J. Chory, George M. Church, Eric D. Brown, Tommi S. Jaakkola, Regina Barzilay, James J. Collins
The computer model, which can screen more than a hundred million chemical compounds in a matter of days, is designed to pick out potential antibiotics that kill bacteria using different mechanisms than those of existing drugs.
Cell, 20 Feb 2020; 180(4): 688-702, https://www.cell.com/ cell/fulltext/S0092-8674(20)30102-1?_returnURL=https% 3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii %2FS0092867420301021%3Fshowall%3Dtrue, accessed 2 Mar. 2020. Summary Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub – halicin – that is structurally divergent from conventional antibiotics and displays bactericidal activity against a wide phylogenetic spectrum of pathogens including Mycobacterium tuberculosis and carbapenem-resistant Enterobacteriaceae. Halicin also effectively treated Clostridioides difficile and pan-resistant Acinetobacter baumannii infections in murine models. Additionally, from a discrete set of 23 empirically tested predictions from >107 million molecules curated from the ZINC15 database, our model identified eight antibacterial compounds that are structurally distant from known antibiotics. This work highlights the utility of deep learning approaches to expand our antibiotic arsenal through the discovery of structurally distinct antibacterial molecules. From: Artificial Intelligence Yields New Antibiotic: A DeepLearning Model Identifies a Powerful New Drug that Can Kill Many Species of Antibiotic-Resistant Bacteria by Anne Trafton, MIT News, 20 Feb. 2020, http://news.mit.edu/2020/artificialintelligence-identifies-new-antibiotic-0220, accessed 2 Mar. 2020. Using a machine-learning algorithm, MIT researchers have identified a powerful new antibiotic compound. In laboratory tests, the drug killed many of the world’s most problematic 50
“We wanted to develop a platform that would allow us to harness the power of artificial intelligence to usher in a new age of antibiotic drug discovery,” says James Collins, the Termeer Professor of Medical Engineering and Science in MIT’s Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering. “Our approach revealed this amazing molecule which is arguably one of the more powerful antibiotics that has been discovered.” In their new study, the researchers also identified several other promising antibiotic candidates, which they plan to test further. They believe the model could also be used to design new drugs, based on what it has learned about chemical structures that enable drugs to kill bacteria. “The machine learning model can explore, in silico, large chemical spaces that can be prohibitively expensive for traditional experimental approaches,” says Regina Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). Barzilay and Collins, who are faculty co-leads for MIT’s Abdul Latif Jameel Clinic for Machine Learning in Health (J-Clinic), are the senior authors of the study, which appears today in Cell. The first author of the paper is Jonathan Stokes, a postdoc at MIT and the Broad Institute of MIT and Harvard.
Self-Contained Neuromusculoskeletal Arm Prostheses Max Ortiz-Catalan, Ph.D., Enzo Mastinu, Ph.D., Paolo Sassu, M.D., Oskar Aszmann, M.D., and Rickard Brånemark, M.D., Ph.D. N Engl J Med 30 Apr 2020; 382:1732-1738, https://www.nejm. org/doi/full/10.1056/NEJMoa1917537, accessed 7 May 2020.
Cryonics / 2nd Quarter 2020
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