University of Oslo and Oslo University Hospital, Department of Pathology, Rikshospitalet, Sognsvannsveien 20, 0372, Oslo, Norway.
University of Lübeck and University Medical Center Schleswig-Holstein, Lübeck Institute of Experimental Dermatology, Medical Systems Biology Division, Medicinal Chemistry and Systems Polypharmacology, Ratzeburger Allee 160, 23538, Lübeck, Germany.
Sci Data. 2024 May 23;11(1):530. doi: 10.1038/s41597-024-03343-8.
The identification of lead molecules and the exploration of novel pharmacological drug targets are major challenges of medical life sciences today. Genome-wide association studies, multi-omics, and systems pharmacology steadily reveal new protein networks, extending the known and relevant disease-modifying proteome. Unfortunately, the vast majority of the disease-modifying proteome consists of 'orphan targets' of which intrinsic ligands/substrates, (patho)physiological roles, and/or modulators are unknown. Undruggability is a major challenge in drug development today, and medicinal chemistry efforts cannot keep up with hit identification and hit-to-lead optimization studies. New 'thinking-outside-the-box' approaches are necessary to identify structurally novel and functionally distinctive ligands for orphan targets. Here we present a unique dataset that includes critical information on the orphan target ABCA1, from which a novel cheminformatic workflow - computer-aided pattern scoring (C@PS) - for the identification of novel ligands was developed. Providing a hit rate of 95.5% and molecules with high potency and molecular-structural diversity, this dataset represents a suitable template for general deorphanization studies.
鉴定先导化合物和探索新的药理学药物靶点是当今医学生命科学的主要挑战。全基因组关联研究、多组学和系统药理学不断揭示新的蛋白质网络，扩展了已知的相关疾病修饰蛋白质组。不幸的是，绝大多数疾病修饰蛋白质组由“孤儿靶点”组成，其内在配体/底物、（病理）生理作用和/或调节剂尚不清楚。不可成药性是当今药物开发的主要挑战，而药物化学的努力跟不上命中鉴定和命中到先导优化研究。需要新的“跳出框框”的方法来识别结构新颖和功能独特的孤儿靶点配体。在这里，我们提供了一个独特的数据集，其中包含了 ABCA1 孤儿靶点的关键信息，从中开发了一种新的计算化学方法——计算机辅助模式评分（C@PS），用于鉴定新的配体。该数据集的命中率为 95.5%，具有高活性和分子结构多样性的分子，代表了一般去孤儿化研究的合适模板。
Zotero 插件
