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

理性计算设计在当今社会中扮演着越来越重要的角色，并被广泛应用于例如建筑和汽车工业中，以减少与传统实验相关的成本。如果我们要将同样的原则应用于药物分子的设计，那么就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，哪些实际上具有治疗益处。理想情况下，计算机程序将能够仅使用已建立的物理定律来执行此功能，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建模被称为第一性原理模拟。今天，包括微电子和可再生能源在内的许多行业的研究人员都使用第一原理模拟来快速扫描多种假设的材料成分。只有在发现了一组符合所需性能的材料后，在实验室中制造这些材料的昂贵过程才会开始。那么，为什么相同的第一原理技术没有被用于设计新的药物分子呢？早在20世纪初，量子力学方程就被记录下来，并被证明能以惊人的精度描述材料的原子尺度行为。因此，答案不是缺乏对物理的理解。相反，这在很大程度上是一个计算问题，需要对药物分子与其治疗靶点之间的相互作用中涉及的大量原子进行建模。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，结构的均匀性意味着块材料可以用两个原子周期性地在3D中重复表示，并且计算模拟这个简单系统的性质是一个相对简单的问题。相比之下，生物系统要复杂得多，为了准确预测分子结构和功能之间的关系，我们经常需要模拟成千上万个原子。然而，由于计算机能力的提高，更重要的是，软件设计的基本进步，第一性原理方法现在可以以与研究硅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。我的想法是放弃这些参数，而是直接从生物治疗目标的第一性原理量子力学模拟中计算它们，比如与疾病有关的蛋白质。这些新的模型参数，而不是通用的，将具体到所研究的系统，从而将改变计算生物分子建模的准确性。改进后的计算模型将用于扫描数百种潜在的药物分子以获得治疗效果，从而使我们能够合理、快速地设计新的候选治疗药物。医学研究人员将能够将他们的设计工作集中在只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，用于设计新的癌症治疗方法。

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.