Knime workflow and data files to calculate a lead-likeness evaluation function 'Λ' for small molecule compounds

A simple and rational method to rank molecules’ lead-likeness using continuous evaluation functions was developed to support epigenetic drug discovery. This strategy proved to be highly effective on model chemical libraries and finally helped driving synthetic efforts towards candidates of interest for epigenetic applications towards HDAC6, BRD4 and EZH2. Here we provide sample data and the knime workflow for generatating lead-like chemical probes for epigenetics. This tool and example data set will enable researchers to establish this workflow in their own laboratories and apply it to new synthetic scaffolds for medicinal chemistry.本研究开发了一种基于连续评估函数（continuous evaluation functions）、用于排序分子类先导化合物（lead-likeness）属性的简洁合理方法，以支撑表观遗传药物研发。该策略在模型化学库中展现出极佳的应用效果，最终推动合成研究工作聚焦于可应用于表观遗传领域、针对HDAC6、BRD4及EZH2靶点的目标候选化合物。 本次公开数据集提供了用于生成表观遗传领域类先导化合物化学探针的示例数据及KNIME工作流。本工具与示例数据集可帮助研究人员在各自实验室搭建该工作流，并将其应用于药物化学领域的新型合成骨架研发工作中。