Department of Pharmaceutical Sciences, University of Vienna, Josef Holaubek Platz 2, 1090, Vienna, Austria.
Research Platform NeGeMac-Next Generation Macrocycles to Address Challenging Protein Interfaces, University of Vienna, 1090, Vienna, Austria.
Mol Inform. 2024 May;43(5):e202300287. doi: 10.1002/minf.202300287. Epub 2024 Mar 5.
In the past years the interest in Solute Carrier Transporters (SLC) has increased due to their potential as drug targets. At the same time, macrocycles demonstrated promising activities as therapeutic agents. However, the overall macrocycle/SLC-transporter interaction landscape has not been fully revealed yet. In this study, we present a statistical analysis of macrocycles with measured activity against SLC-transporter. Using a data mining pipeline based on KNIME retrieved in total 825 bioactivity data points of macrocycles interacting with SLC-transporter. For further analysis of the SLC inhibitor profiles we developed an interactive KNIME workflow as well as an interactive map of the chemical space coverage utilizing parametric t-SNE models. The parametric t-SNE models provide a good discrimination ability among several corresponding SLC subfamilies' targets. The KNIME workflow, the dataset, and the visualization tool are freely available to the community.
在过去的几年中,由于 Solute Carrier Transporters(SLC)作为药物靶点的潜力,人们对其的兴趣日益增加。与此同时,大环化合物作为治疗剂表现出了有前景的活性。然而,大环化合物/SLC-转运体相互作用的全貌尚未完全揭示。在这项研究中,我们对具有 SLC-转运体活性测量的大环化合物进行了统计分析。使用基于 KNIME 的数据挖掘管道,总共检索到了 825 个大环化合物与 SLC-转运体相互作用的生物活性数据点。为了进一步分析 SLC 抑制剂的特性,我们开发了一个交互式 KNIME 工作流程以及利用参数 t-SNE 模型的化学空间覆盖交互式地图。参数 t-SNE 模型在几个相应的 SLC 亚家族靶标之间具有很好的区分能力。KNIME 工作流程、数据集和可视化工具都可供社区免费使用。