摘要
一位新闻记者-机器人与机器学习的新闻编辑-每日新闻-关于人工智能的研究结果在一份新的报告中讨论。根据NewsRx记者在新泽西州Piscatawa Y的新闻报道，研究表明：“食欲素1受体（O 1R）是一种G蛋白偶联受体，通过与神经肽食欲素A和B的相互作用调节多种生理过程。”这项研究的财政支持者包括国家药物滥用研究所。新闻记者引用了新泽西医学与牙科大学(UMDNJ)的一项研究：“选择性OX1R拮抗剂在包括寻求药物和暴饮暴食在内的多种行为障碍的临床前模型中表现出治疗作用。然而，目前还没有获得临床批准的选择性OX1R Anta Gonists，这加剧了对作为这一目标的新型化合物的需求。在这项研究中，我们使用严格过滤器和标准级联精心策划了包含1300多个OX1R配体的数据集。采用优化的随机森林机器学习算法超参数和5倍交叉验证递归有限元消去法筛选出12个二维分子描述子,建立了高预测定量构效关系(QSAR)模型,并通过外部测试集和丰富的研究进一步评价了该模型的预测能力。通过对药物库数据库的虚拟筛选，验证了该模型的实用性。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news reporting from Piscatawa y, New Jersey, by NewsRx journalists, research stated, "The orexin 1 receptor (O X1R) is a G-protein coupled receptor that regulates a variety of physiological p rocesses through interactions with the neuropeptides orexin A and B." Financial supporters for this research include National Institute on Drug Abuse. The news reporters obtained a quote from the research from University of Medicin e and Dentistry of New Jersey (UMDNJ): "Selective OX1R antagonists exhibit thera peutic effects in preclinical models of several behavioral disorders, including drug seeking and overeating. However, currently there are no selective OX1R anta gonists approved for clinical use, fueling demand for novel compounds that act a t this target. In this study, we meticulously curated a dataset comprising over 1300 OX1R ligands using a stringent filter and criteria cascade. Subsequently, w e developed highly predictive quantitative structureactivity relationship (QSAR ) models employing the optimized hyper-parameters for the random forest machine learning algorithm and twelve 2D molecular descriptors selected by recursive fea ture elimination with a 5-fold cross-validation process. The predictive capacity of the QSAR model was further assessed using an external test set and enrichmen t study, confirming its high predictivity. The practical applicability of our fi nal QSAR model was demonstrated through virtual screening of the DrugBank databa se."
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