Department of Pathology, Section of Neuropathology, Translational Neurodegeneration Research and Neuropathology Lab (www.pahnkelab.eu), University of Oslo and Oslo University Hospital, Sognsvannsveien 20, 0372, Oslo, Norway.
Department of Pharmaceutical and Cellbiological Chemistry, Pharmaceutical Institute, University of Bonn, An der Immenburg 4, 53121, Bonn, Germany.
Sci Data. 2022 Jul 26;9(1):446. doi: 10.1038/s41597-022-01506-z.
Multitarget datasets that correlate bioactivity landscapes of small-molecules toward different related or unrelated pharmacological targets are crucial for novel drug design and discovery. ATP-binding cassette (ABC) transporters are critical membrane-bound transport proteins that impact drug and metabolite distribution in human disease as well as disease diagnosis and therapy. Molecular-structural patterns are of the highest importance for the drug discovery process as demonstrated by the novel drug discovery tool 'computer-aided pattern analysis' ('C@PA'). Here, we report a multitarget dataset of 1,167 ABC transporter inhibitors analyzed for 604 molecular substructures in a statistical binary pattern distribution scheme. This binary pattern multitarget dataset (ABC_BPMDS) can be utilized for various areas. These areas include the intended design of (i) polypharmacological agents, (ii) highly potent and selective ABC transporter-targeting agents, but also (iii) agents that avoid clearance by the focused ABC transporters [e.g., at the blood-brain barrier (BBB)]. The information provided will not only facilitate novel drug prediction and discovery of ABC transporter-targeting agents, but also drug design in general in terms of pharmacokinetics and pharmacodynamics.
多靶标数据集对于新型药物设计和发现至关重要，这些数据集与小分子对不同相关或不相关的药理学靶标的生物活性图谱相关联。ATP 结合盒（ABC）转运蛋白是关键的膜结合转运蛋白，它们会影响人类疾病中的药物和代谢物分布，以及疾病的诊断和治疗。分子结构模式对于药物发现过程至关重要，正如新型药物发现工具“计算机辅助模式分析”（C@PA）所证明的那样。在这里，我们报告了一个针对 604 种分子亚结构进行分析的 1167 种 ABC 转运蛋白抑制剂的多靶标数据集，采用了统计二进制模式分布方案。这个二进制模式多靶标数据集（ABC_BPMDS）可用于各种领域。这些领域包括（i）多药理学药物的设计、（ii）高效且选择性的 ABC 转运蛋白靶向药物的设计，以及（iii）避免被特定 ABC 转运蛋白清除的药物的设计[例如，在血脑屏障（BBB）中]。提供的信息不仅将促进 ABC 转运蛋白靶向药物的新型药物预测和发现，还将促进药物设计在药代动力学和药效学方面的发展。
