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

大规模量子力学模拟在未来药物疗法开发中的应用

• 批准号: EP/R010153/1
• 负责人: Daniel Cole
• 金额: $ 12.57万
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FR010153%2F1
• 关键词: Application; large; scale; quantum; mechanical

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.

理性的计算设计在当今社会中起着越来越重要的作用，并且在例如建筑和汽车行业中广泛使用，以降低与常规实验相关的成本。如果我们要在制药分子的设计中应用相同的原理，那么有必要能够高精度地预测哪种分子中我们可以在实验室中可能合成的多种分子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执行此功能，而不是依靠实验测量的数据输入。原子在此基本层面上的建模称为第一原理模拟。如今，许多行业的研究人员（包括微电子和可再生能源）使用了模拟，以迅速扫描了许多假设材料组成的群众。只有发现一组与所需属性相匹配的材料，在实验室中制造这些材料的昂贵过程是否开始。那么，为什么不使用相同的第一原理技术来设计新的药物分子？写下了量子力学的方程式，并显示出早在20世纪初就以显着准确性描述材料的原子级行为。因此，答案不是缺乏身体上的理解。取而代之的是，这在很大程度上是建模药物分子之间相互作用的大量原子所需的问题的问题在计算上对此简单系统的属性进行建模。相比之下，生物系统更为复杂，我们通常需要模拟数千种原子，以便准确预测分子的结构及其功能之间的关系。但是，由于计算机功率的提高以及更重要的是，软件设计的基本进步，第一原理方法现在可以使用与研究硅的准确性相同的准确性来访问这些生物系统。计算药物发现的传统方法依赖于数百个模型参数的计算药物发现，这些方法已从实验中从实验中收集的数百个模型参数。我的想法是分配这些参数，而是直接从生物学治疗靶标的第一原理量子机械模拟中计算出它们，例如与疾病有关的蛋白质。这些新的模型参数，而不是通用，将针对正在研究的系统进行特定，从而改变计算生物分子建模的准确性。改进的计算模型将用于扫描数百个潜在的药物分子以进行治疗益处，从而使我们能够合理和快速设计新的治疗候选者。医学研究人员将能够将他们的设计工作集中在合成最有希望的分子上，从而提高了药物开发初期成功的可能性，并降低了对患者的药物成本。这个概念将与纽卡斯尔大学北部癌症研究所合作进行实践，以设计新颖的癌症疗法。

项目成果

Development and Validation of the QUBE Protein Force Field

• DOI: 10.26434/chemrxiv.7565222.v2
• 发表时间: 2019-05
• 作者: A. Allen;M. J. Robertson;M. Payne;Daniel Cole

Implementation of the QUBE Force Field in SOMD for High-Throughput Alchemical Free-Energy Calculations.

在 SOMD 中实施 QUBE 力场以进行高通量炼金术自由能计算。

• DOI: 10.1021/acs.jcim.1c00328
• 发表时间: 2021
• 期刊: Journal of chemical information and modeling
• 影响因子: 5.6
• 作者: Nelson L

Implementation of the QUBE Force Field in SOMD for High-Throughput Alchemical Free Energy Calculations

在 SOMD 中实施 QUBE 力场以进行高通量炼金术自由能计算

• DOI: 10.26434/chemrxiv.13116878.v2
• 发表时间: 2021
• 作者: Nelson L