首页|University of Maryland School of Pharmacy Reports Findings in Machine Learning (Machine Learning Models to Interrogate Proteome-Wide Covalent Ligandabilities Directed at Cysteines)
University of Maryland School of Pharmacy Reports Findings in Machine Learning (Machine Learning Models to Interrogate Proteome-Wide Covalent Ligandabilities Directed at Cysteines)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - New research on Machine Learning is th e subject of a report. According to newsreporting out of Baltimore, Maryland, b y NewsRx editors, research stated, “Machine learning (ML) identificationof cova lently ligandable sites may accelerate targeted covalent inhibitor design and he lp expandthe druggable proteome space. Here, we report the rigorous development and validation of the tree-basedmodels and convolutional neural networks (CNNs ) trained on a newly curated database (LigCys3D) ofover 1000 liganded cysteines in nearly 800 proteins represented by over 10,000 three-dimensional structuresin the protein data bank.”Financial support for this research came from National Cancer Institute.
BaltimoreMarylandUnited StatesNort hand Central AmericaCyborgsEmerging TechnologiesMachine LearningPeptides and ProteinsProteinsProteome
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