Data-driven drug discovery: investigating the molecular mechanisms of safety and efficacy

摘要 高达30%的药物不良事件可归因于代谢或机制中的药物-药物相互作用 行动。然而，对驱动这些不良反应的药物-药物-蛋白质相互作用的研究仍然是一个 未被开发的研究领域。这项研究项目旨在提供第一批全面的见解之一 基因变异对药物不良事件的影响，特别关注药物与药物的相互作用。穿过 利用大数据集和计算方法，我们的目标是开发一种数据驱动的方法来识别 靶蛋白和代谢蛋白的遗传变异与药物不良反应的关系 事件。在我们目前工作的基础上，我们将识别影响感兴趣蛋白质的遗传变异， 参与研究的参与者经历了药物使用和行为后的不良药物事件 关联研究，以确定任何重要的变异。

Summary Up to 30% of adverse drug events can be attributed to drug-drug interactions in metabolism or mechanism of action. The study of drug-drug-protein interactions that drive these adverse effects, however, remains an underexplored area of research. This research project aims to offer one of the first comprehensive insights into the effect of genetic variants on adverse drug events, with a particular focus on drug-drug interactions. Through the utilization of large datasets and computational methods, we aim to develop a data-driven approach to identify genetic variations of target protein and metabolizing proteins associated with the development of adverse drug events. Expanding from our current work, we will identify genetic variants affecting the proteins of interest, participants in the study who have experienced the adverse drug events following use of the drugs and conduct association studies to identify any significant variants.