摘要
由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。据新闻报道新闻Rx记者从智利圣地亚哥报道，研究称，“这项研究使机器协同起来。”利用概念密度泛函理论(CDFT)学习(ML)开发符合oecd的预测ive芳香胺(AAs)致突变活性的全no-code模型251原子吸收光谱、留一出交叉氧化(LOOCV)和三种不同数据的综合数据集劈腿我们的研究采用了GFN2-xtb方法来计算真空和水相中致癌原及其活化代谢物的描述符。
Abstract
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.”
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