摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-调查人员讨论机器学习的新发现。根据NewsRx记者在文莱加东的新闻报道，研究表明：“在药物发现中，虚拟筛选是识别潜在HIT化合物的关键。本研究旨在展示一个采用机器学习模型的高级管道，将各种常规筛选方法结合起来。”这项研究的财政支持来自交通和信息通信部下属的技术和科学研究和高级理事会(CREATES)mtic/creates(MTIC)。新闻记者引用了来自Bru Nei Dasuran大学的研究，“选择了不同的蛋白质靶阵列，在使用四种不同的方法评分之前，对它们对应的数据集进行活性/诱骗分布分析：QSAR、药效团、对接和二维形状相似性，采用新的W_new公式对微调后的机器学习模型进行排序，计算一致性分数，并对每个目标进行富集研究，结果表明，一致性评分在特异性蛋白靶标PARG和DPP4上优于其他方法，AUC值分别为0.90和0.84，值得注意的是，在特定蛋白靶标PARG和DPP4上，一致性评分优于其他方法，AUC值分别为0.90和0.84.与所有其他筛选方法相比，这种方法始终优先考虑具有更高实验PIC50值的化合物。此外，在外部验证过程中，模型显示出在2值方面的中等至高性能范围。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning. According to news reporting originating in Gadong, Brunei, by NewsRx journalists, research stated, "In drug discovery, virtual screening is cr ucial for identifying potential hit compounds. This study aims to present a nove l pipeline that employs machine learning models that amalgamates various convent ional screening methods." Financial support for this research came from Council for Research and Advanceme nt in Technology and Science (CREATES) MTIC/CREATES under the Ministry of Transp ort and Infocommunications (MTIC). The news reporters obtained a quote from the research from the University of Bru nei Darussalam, "A diverse array of protein targets was selected, and their corr esponding datasets were subjected to active/decoy distribution analysis prior to scoring using four distinct methods: QSAR, Pharmacophore, docking, and 2D shape similarity, which were ultimately integrated into a single consensus score. The fine-tuned machine learning models were ranked using the novel formula 'w_ new', consensus scores were calculated, and an enrichment study was performed fo r each target. Distinctively, consensus scoring outperformed other methods in sp ecific protein targets such as PPARG and DPP4, achieving AUC values of 0.90 and 0.84, respectively. Remarkably, this approach consistently prioritized compounds with higher experimental PIC50 values compared to all other screening methodolo gies. Moreover, the models demonstrated a range of moderate to high performance in terms of R2 values during external validation."
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