Chillin in Flatland

小分子疗法领域的创新正变得越来越困难。特征明确、易于药物靶点的“低垂果实”几乎都已采摘完毕，但迅速开发有效新药的必要性仍是一个紧迫的问题。最近的冠状病毒大流行和不断发展的抗菌素耐药性危机就是这一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库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，允许对杂环异构体的研究，以发现新的核心和支架，用于化合物优化和产生新的化学物种。有人提出3D分子更有可能成为成功的药物，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，选择性地引入每个SP3碳所需的合成化学是缓慢的，并且其范围有限。杂环芳香族化合物被广泛使用，其合成在铅的优化和药物开发中已为人们所熟知。它们的物理化学性质更容易调节，而且它们通常类似于细胞代谢物和信号分子。2009年，皮特等人。一个数据库，其中有25000个潜在的可获得的小杂环，据报道，其中只有1100个被合成了。我们建议开发一个计算工具(杂环等位探索者)来发现新的杂环核心，通过比较输入分子的形状和静电势与载体数据库中每个成员的形状和静电势，并返回最佳的生物等位元替代。这将允许快速获得新的类药物分子，但也可以用来确定sp2空间的未被探索的区域，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产生分子，然后进行合成和分析，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后，目标是开发工具机器学习模型并将其纳入工具机器学习模型，该模型可以使用实验证据来指导预测。这也将属于人工智能的科学研究领域和主题。

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.