Synthesis and Structure Elucidation of Natural Products

在这个项目中，我们的目标是使被称为聚酮化合物的化合物的制备自动化，这样机器人就可以被编程来制作整个聚酮化合物库，而几乎不需要人工干预。由于这个项目将允许我们在短时间内获得数十或数百个聚酮类化合物，我们还将开发基于量子力学和机器学习的非常快速的计算工具，以设计聚酮类化合物，并在制造完成后对其进行分析。聚酮类化合物是一类自然产生的化学物质，约占当前最畅销药物的20%，包括抗生素、抗真菌药物和抗肿瘤药物。因此，它们对人类健康至关重要，它们的制备(一种被称为“化学合成”的过程)是20世纪的主要成功之一。然而，这些分子的结构可能非常复杂--它们的化学合成具有难以置信的挑战性，事实上，每个聚酮的合成都是一项单独的、定制的科学研究，需要几个月、几年或几十年的工作才能完成。即使当它们被制造出来时，它们的复杂性也使得对它们的结构和行为的研究成为另一项具有挑战性的任务--例如，如果我们希望了解它们的三维结构和运动，那么我们经常不得不依赖要求苛刻的量子化学计算，这需要数天/数周/数月的高端计算时间来进行。所有这一切都与其他重要的自然存在的分子形成了鲜明的对比，比如多肽(构成人体蛋白质)或DNA。对于这些分子，化学合成现在完全是例行公事，可以完全自动化--科学家只需拨出他们想要的化合物，机器人就可以用简单、容易获得的积木来构建它。我们希望使非常有价值的聚酮类化合物变得更容易获得。我们已经开发出制造聚酮类化合物的方法，现在寻求在我们新收购的化学自动化平台--一种机器人化学合成仪器--上自动组装它们。通过组合机器人上的不同构件，可以快速获得一组不同的复杂聚酮，这反过来将使生物学研究能够了解该结构是否会影响生物活性。制造如此多新的、复杂的化合物的不利之处，就是在设计或分析我们想要制造的分子结构--特别是它们的三维结构--时造成的瓶颈。我们目前最先进的量子化学方法在这方面的速度非常慢(但非常准确)，而且根本跟不上。因此，我们建议在我们最近开发的超高速机器学习系统的基础上，该系统可以模拟量子化学计算，但只需几毫秒，而不是几天或几个月。我们制造的机器人合成和得到的化合物将使我们能够开发和测试更准确的量子化学方法，然后使用这些方法来大规模改进我们的机器学习系统，以便它足够好地快速筛选我们可能合成的数百甚至数千个潜在结构。有了制造聚酮的自动合成机器人和允许我们设计和研究这些化合物的机器学习系统，我们最终的目标是使聚酮合成像肽和DNA合成一样容易-彻底改变这些分子的开发方式。

In this project we will aim to automate the preparation of chemical compounds called polyketides, so that a robot can be programmed to make an entire library of polyketide compounds with little or no human intervention. Because this project will allow us to access dozens or hundreds of polyketides in a short space of time, we will also be developing very fast computational tools based on quantum mechanics and machine learning to design polyketides and then analyse them once they are made.Polyketides are a class of naturally occurring chemicals that comprise around 20% of the current top-selling drugs, including antibiotics, antifungals and anti-tumour agents. As such they are crucially important to human health and preparation of them (a process called "chemical synthesis") has been one of the major successes of the 20th century. However, the structures of these molecules can be extremely complex - and their chemical synthesis is incredibly challenging, in fact the synthesis of each polyketide is a separate, bespoke scientific investigation taking months, years or decades of work to complete. Even when they are made, their complexity makes the study of their structures and behaviours another challenging task - for example if we wish to understand their 3-dimensional structure and motion, then we often have to rely on demanding quantum chemical calculations that require days/weeks/months of high-end computing time to undertake. All of this contrasts with other important naturally occurring molecules, such as peptides (which make up the proteins in the body) or DNA. For these molecules, the chemical synthesis is now totally routine and can be fully automated - the scientist simply dials-up the compound they want and the robot can construct it from simple, readily available building blocks. We want to make the incredibly valuable polyketide class of compounds just as accessible.We have developed methodology for making the components of polyketides and now seek to automate their assembly on our newly acquired Chemspeed Automated Platform - a robotic chemical synthesis instrument. Through combining different building blocks on the robot, a diverse set of complex polyketides can be rapidly accessed, which in turn will enable biological studies to see if how the structure affects the biological activity. The downside of making so many new, complex chemical compounds, is then the bottle-neck created in designing or analysing the structures of the molecules we would like to make - in particular their three-dimensional structures. Our current state-of-the-art quantum chemical approaches to this are very slow (but incredibly accurate) and will simply not be able to keep up. So we propose to build on our recent development of an ultra-fast machine learning system that can mimic quantum chemical calculations, but in milliseconds rather than days or months. The robotic syntheses and resulting compounds that we make will allow us to develop and test more accurate quantum chemical methods and then use these to massively improve our machine learning system so that it is good enough to rapidly screen the hundreds or even thousands of potential structures that we might synthesise. With both the automated synthesis robot to make polyketides and the machine learning system that lets us design and study these compounds we ultimately aim to render polyketide synthesis as easy as peptide and DNA synthesis - revolutionising the way that these molecules are developed.