Application of large-scale quantum mechanical simulation to the development of future drug therapies

合理的计算设计在当今社会发挥着越来越重要的作用，并被广泛应用于建筑和汽车行业，以降低与传统实验相关的成本。如果我们要将同样的原理应用于药物分子的设计，那么有必要能够高精度地预测我们在实验室中可能合成的众多分子中哪些确实具有治疗效果。理想情况下，计算机程序应该能够仅使用既定的物理定律来执行这一功能，而不是依赖于实验测量的数据输入。在这个基本水平上对原子的模拟被称为第一原理模拟。第一原理模拟目前被包括微电子和可再生能源在内的许多行业的研究人员用来快速扫描大量假设的材料成分。只有在发现了一组与所需特性匹配的材料后，才会开始在实验室中制造这些材料的昂贵过程。那么，为什么同样的第一原理技术不被用于设计新的药物分子呢？早在二十世纪初，量子力学方程就被记载下来，并被证明能够以惊人的精度描述材料的原子尺度行为。因此，答案并不是缺乏身体上的理解。相反，这在很大程度上是一个对药物分子与其治疗靶点之间相互作用所涉及的大量原子进行建模所需的计算工作量的问题。在典型的现代电子设备中，硅原子的数量是不可想象的，但重要的是，结构的均质性意味着主体材料可以由两个在3D中周期性重复的原子来表示，对这个简单系统的性质进行计算建模是一个相对简单的问题。相比之下，生物系统要复杂得多，我们通常需要模拟数千个原子，才能准确预测分子结构和功能之间的关系。然而，由于计算机能力的增加，以及更重要的是软件设计的根本进步，第一原理方法现在可以以与研究硅分子完全相同的精度访问这些生物系统。传统的计算药物发现方法严重依赖于几十年来从小分子实验或计算分析中收集的数百个模型参数。我的想法是省去这些参数，直接根据生物治疗目标的第一原理量子力学模拟来计算它们，例如与疾病有关的蛋白质。这些新的模型参数不是通用的，而是特定于正在研究的系统的，因此将改变计算生物分子建模的准确性。改进后的计算模型将被用于扫描数百个潜在的药物分子的治疗效益，从而使我们能够合理和快速地设计新的治疗候选药物。医学研究人员将能够将他们的设计努力集中在仅合成最有希望的分子上，从而提高在药物开发的早期阶段成功的可能性，并降低患者的药物成本。这一概念将与纽卡斯尔大学北方癌症研究所合作实施，以设计新的癌症疗法。

Rational computational design plays an increasingly important role in today's society, and is widely used in, for example, the construction and automotive industries to reduce costs associated with conventional experiments. If we are to apply the same principles to the design of pharmaceutical molecules, then it is necessary to be able to predict with high accuracy which of the multitude of molecules that we can potentially synthesise in the lab actually have therapeutic benefits. Ideally, the computer program would be able to perform this function using only established laws of physics, rather than relying on data input from experimental measurements. The modelling of atoms at this fundamental level is known as first principles simulation.First principles simulations are used today by researchers in many industries, including microelectronics and renewable energy, to rapidly scan multitudes of hypothetical material compositions. Only once a set of materials matching the desired properties is discovered, does the costly process of manufacturing those materials in the lab begin. So why are the same first principles techniques not used to design new pharmaceutical molecules? The equations of quantum mechanics were written down and shown to describe the atomic-scale behaviour of materials with remarkable accuracy as early as the beginning of the twentieth century. Therefore, the answer is not a lack of physical understanding. Instead, it is largely a problem of the computational effort required to model the large numbers of atoms that are involved in interactions between a pharmaceutical molecule and its therapeutic target.There are an unimaginable number of silicon atoms in typical modern electronic devices, but importantly the homogeneity of the structures means that the bulk material can be represented by just two atoms periodically repeated in 3D, and it is a relatively straightforward problem to computationally model the properties of this simple system. In contrast, biological systems are much more complex and often we need to simulate many thousands of atoms in order to accurately predict the relationships between the molecule's structure and its function. However, due to increases in computer power and, more importantly, fundamental advances in software design, first principles approaches can now access these biological systems with precisely the same accuracy that is used to study silicon.Traditional approaches to computational drug discovery rely heavily on hundreds of model parameters that have been collected over many decades from experiments or computational analysis of small molecules. My idea is to dispense with these parameters and instead compute them directly from first principles quantum mechanical simulations of the biological therapeutic target, such as a protein that is implicated in disease. These new model parameters, rather than being generic, will be specific to the system under study and will thereby transform the accuracy of computational biomolecular modelling. The improved computational models will be used to scan hundreds of potential pharmaceutical molecules for therapeutic benefit, thus allowing us to rationally and rapidly design new therapeutic candidates. Medical researchers will be able to focus their design efforts on synthesising only the most promising molecules, thereby improving the likelihood of success in the early stages of pharmaceutical development and decreasing the cost of medicines to the patient. This concept will be put into practice in collaboration with the Northern Institute for Cancer Research at Newcastle University for the design of novel cancer therapies.