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

总结 高达30%的药物不良事件可归因于代谢中的药物间相互作用或代谢机制。 行动上然而，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挑战。 探索不足的研究领域。该研究项目旨在提供第一个全面的见解， 遗传变异对药物不良事件的影响，特别关注药物相互作用。通过 利用大型数据集和计算方法，我们的目标是开发一种数据驱动的方法来识别 与药物不良反应发生相关的靶蛋白和代谢蛋白的遗传变异 事件从我们目前的工作扩展，我们将确定影响感兴趣的蛋白质的遗传变异， 在使用药物后发生药物不良事件的研究参与者， 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.