机器学习
虚拟筛选
对接(动物)
托法替尼
随机森林
人工智能
分子描述符
计算机科学
药物重新定位
支持向量机
药物发现
IC50型
化学信息学
数量结构-活动关系
药品
计算生物学
化学
药理学
生物
生物化学
医学
计算化学
体外
护理部
免疫学
类风湿性关节炎
作者
Muhammad Yasir,Jinyoung Park,Eun‐Taek Han,Won Sun Park,Jin‐Hee Han,Yong-Soo Kwon,Hee Jae Lee,Wanjoo Chun
标识
DOI:10.1021/acs.jcim.3c01090
摘要
values from the prediction were reported to possess JAK inhibition activity, which indicates the limitations of the prediction model. To confirm the JAK2 inhibition activity of predicted compounds, molecular docking and molecular dynamics simulation were carried out with the JAK inhibitor reference compound, tofacitinib. The binding affinity of docked compounds in the active region of JAK2 was also analyzed by the gmxMMPBSA approach. Furthermore, experimental validation confirmed the results from the computational analysis. Results showed highly comparable outcomes concerning tofacitinib. Conclusively, the machine learning model can efficiently improve the virtual screening of drugs and drug development.
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