Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern.
Chimia (Aarau). 2022 Dec 21;76(12):1045-1051. doi: 10.2533/chimia.2022.1045.
Similar drug molecules often have similar properties and activities. Therefore, quantifying molecular similarity is central to drug discovery and optimization. Here I review computational methods using molecular similarity measures developed in my group within the interdisciplinary network NCCR TransCure investigating the physiology, structural biology and pharmacology of ion channels and membrane transporters. We designed a 3D molecular shape and pharmacophore comparison algorithm to optimize weak and unselective inhibitors by scaffold hopping and discovered potent and selective inhibitors of the ion channels TRPV6 and TRPM4, of endocannabinoid membrane transport, and of the divalent metal transporters DMT1 and ZIP8. We predicted off-target effects by combining molecular similarity searches from different molecular fingerprints against target annotated compounds from the ChEMBL database. Finally, we created interactive chemical space maps reflecting molecular similarities to facilitate the selection of screening compounds and the analysis of screening results. These different tools are available online at https://gdb.unibe.ch/tools/.
相似的药物分子通常具有相似的性质和活性。因此，量化分子相似性是药物发现和优化的核心。在这里，我回顾了使用我所在的跨学科网络 NCCR TransCure 中开发的分子相似性度量的计算方法，该网络研究离子通道和膜转运体的生理学、结构生物学和药理学。我们设计了一种 3D 分子形状和药效团比较算法，通过支架跳跃对弱和非选择性抑制剂进行优化，并发现了 TRPV6 和 TRPM4 离子通道、内源性大麻素膜转运体以及二价金属转运体 DMT1 和 ZIP8 的有效和选择性抑制剂。我们通过将来自不同分子指纹的分子相似性搜索与 ChEMBL 数据库中标记为靶标的化合物相结合，预测了潜在的副作用。最后，我们创建了交互式化学空间图，反映了分子相似性，以方便筛选化合物的选择和筛选结果的分析。这些不同的工具可在 https://gdb.unibe.ch/tools/ 上在线获得。
