Combining yeast chemical genetics and AI to enable efficient identification of molecules from plants and fungi with cell inhibitory modes-of-action re

结合酵母化学遗传学和人工智能，能够通过细胞抑制作用模式有效识别植物和真菌中的分子

• 批准号: 2868577
• 金额: --
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2868577
• 关键词: Combining; yeast; chemical; genetics; AI

Large living and preserved collections of plants and fungi, such as those found at Kew, represent an extremely valuable resource for drug discovery, but identifying pharmacologically selective molecules from nature that exhibit desirable modes-of-action is currently both time consuming and inefficient. Natural products chemistry needs new efficient approaches to unlock the medicinal potential of plant and fungal compounds and ensure that only compounds with exemplary pharmacological activity undergo chemical isolation. The aim of this project is to test the ability of an artificial intelligence cheminformatics platform to correctly predict which compounds out of all published plant and fungal metabolites, interact selectively with the cancer drug targets mTOR and HSP90 and the Parkinson's disease drug target -synuclein. By combining rapid in silico screening with simple, highly diagnostic yeast assays for these drug targets, we aim to demonstrate a way to sidestep the inefficient experimental approaches that are a current hallmark of the natural products field.

大量现存且保存完好的植物和真菌收藏，例如在邱园发现的植物和真菌，代表了药物发现的极其宝贵的资源，但从自然界中识别具有理想作用模式的药理学选择性分子目前既耗时又低效。天然产物化学需要新的有效方法来释放植物和真菌化合物的药用潜力，并确保只有具有典型药理活性的化合物才能进行化学分离。该项目的目的是测试人工智能化学信息学平台正确预测所有已发表的植物和真菌代谢物中哪些化合物选择性地与癌症药物靶点 mTOR 和 HSP90 以及帕金森病药物靶点 - 突触核蛋白相互作用的能力。通过将快速计算机筛选与针对这些药物靶标的简单、高度诊断的酵母测定相结合，我们的目标是展示一种方法来避开低效的实验方法，而这些方法是天然产物领域当前的标志。