Chillin in Flatland

在平地放松一下

• 批准号: 2451631
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2451631
• 关键词: Chillin; Flatland

Innovation in the field of small molecule therapeutics is becoming increasingly difficult. The 'low hanging fruit' of well characterised, easy to drug targets have almost all been picked, yet the need to quickly develop potent new medicines is still a pressing issue. The recent Coronavirus pandemic, and the developing anti-microbial resistance crisis are two obvious examples of this need. Computational methods are emerging as impactful tools in medicinal chemistry and drug discovery, with the ability to screen and analyse vast libraries of compounds far faster than the laboratory scientist. This project aims to develop an open-source, computational tool that allows the investigation of heterocyclic isosteres to discover new cores and scaffolds for compound optimisation and the generation of novel chemical species. It has been proposed that 3D molecules stand a better chance of becoming successful drugs, however over half of the carbons in these '3D' molecules are sp2 hybridised, and therefore flat. Furthermore the synthetic chemistry required to selectively introduce each sp3 carbon is slow, and limited in its scope. Heteroaromatic systems are widely used, and their synthesis well understood in lead optimisation and drug discovery. Their physicochemical properties are more easily tuned, and they are often similar to cellular metabolites and signalling molecules. In 2009 Pitt et al. published VEHICLe, a database of 25 000 potentially accessible small heterocycles of which only 1100 have been reportedly synthesised. We propose to develop a computational tool (the HeteroCycle Isostere Explorer) to discover new heterocyclic cores for compound optimisation by comparing the shape and electrostatic potentials of input molecules to those of each member of the VEHICLe database, and returning the best bioisosteric replacements. This will allow access to new drug-like molecules quickly, but can also be used to determine underexplored areas of sp2 space, which could yield novel physicochemical properties in therapeutic lead compounds. This project would fall within the EPSRC chemical biology research area, and the software development within the computational chemistry area. The aim is to use the tool to generate molecules which would then be synthesised and assayed to test and refine its predictions, and this would then fall also within the synthetic organic chemistry and synthetic biology research areas. When sufficient experimental results have been accumulated, the goal is to develop and incorporate into the tool machine learning models, which can use experimental evidence to guide the predictions. This would then also fall within the AI for Science research area and theme.

小分子治疗领域的创新变得越来越困难。特征明确、易于药物靶点的“低垂的果实”几乎都已被采摘，但快速开发有效新药的需求仍然是一个紧迫的问题。最近的冠状病毒大流行和不断发展的抗微生物耐药性危机是这种需求的两个明显例子。计算方法正在成为药物化学和药物发现中的有效工具，其筛选和分析大量化合物的能力远远快于实验室科学家。该项目旨在开发一种开源的计算工具，允许对杂环电子等排体进行研究，以发现用于化合物优化和产生新化学物种的新核心和支架。有人提出，3D分子有更好的机会成为成功的药物，然而这些“3D”分子中超过一半的碳是sp2杂化的，因此是平坦的。此外，选择性引入每个sp3碳所需的合成化学是缓慢的，并且其范围有限。杂芳族系统被广泛使用，并且它们的合成在先导物优化和药物发现中被很好地理解。它们的物理化学性质更容易调节，并且它们通常与细胞代谢物和信号分子相似。2009年，Pitt等人发表了VENUCLELE，一个包含25000个潜在可获得的小杂环的数据库，据报道其中只有1100个被合成。我们建议开发一种计算工具（Heterocycle Isostere Explorer），通过将输入分子的形状和静电势与VENOVLE数据库中每个成员的形状和静电势进行比较，并返回最佳的生物电子等排替代品，来发现用于化合物优化的新杂环核心。这将允许快速获得新的药物样分子，但也可以用于确定sp2空间的未开发区域，这可能会在治疗性先导化合物中产生新的物理化学性质。该项目将属于EPSRC化学生物学研究领域，以及计算化学领域的软件开发。其目的是使用该工具生成分子，然后对其进行合成和分析，以测试和完善其预测，然后这也将属于合成有机化学和合成生物学研究领域。当积累了足够的实验结果时，目标是开发并纳入工具机器学习模型，这些模型可以使用实验证据来指导预测。这也将属于AI for Science的研究领域和主题。