首页|Universidad Andres Bello Reports Findings in Machine Learning (Synergizing Machi ne Learning, Conceptual Density Functional Theory, and Biochemistry: No-Code Exp lainable Predictive Models for Mutagenicity in Aromatic Amines)
Universidad Andres Bello Reports Findings in Machine Learning (Synergizing Machi ne Learning, Conceptual Density Functional Theory, and Biochemistry: No-Code Exp lainable Predictive Models for Mutagenicity in Aromatic Amines)
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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 from Santiago, Chile, by News Rx journalists, research stated, “This study synergizes machinelearning (ML) wi th conceptual density functional theory (CDFT) to develop OECD-compliant predict ivemodels for the mutagenic activity of aromatic amines (AAs) with a fully No-C ode methodology using acomprehensive data set of 251 AAs, Leave-One-Out-Cross-V alidation (LOOCV), and three distinct datasplits. Our research employs the GFN2 -xTB method, known for its robustness and speed, to computedescriptors for proc arcinogens and their activated metabolites in vacuum and aqueous phases.”
SantiagoChileSouth AmericaAminesBiochemistryChemistryCyborgsEmerging TechnologiesMachine LearningOrgan ic Chemicals
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