FLF Next generation atomistic modelling for medicinal chemistry and biology

FLF 下一代药物化学和生物学原子建模

• 批准号: MR/Y019601/1
• 负责人: Daniel Cole
• 金额: $ 75.89万
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FY019601%2F1
• 关键词: FLF; Next; generation; atomistic; modelling

The efficiency of drug discovery has been falling for decades such that, for each new molecule that reaches the consumer, estimated research and development costs are in excess of $2 billion. The drug discovery process involves the design of, usually, a small organic molecule that is capable of binding to its target in vivo with therapeutic benefit. Part of the problem is that during this pipeline too many molecules are synthesised in the lab, at great expense, that turn out not to have the required binding affinity. A computational model that is capable of reliably predicting binding from structure is needed.Allied with structural biology methods, such as x-ray crystallography and cryo-electron microscopy, computational structure-based biomolecular simulation is an important part of the solution. At the simplest level, biomolecular simulation can be used to 'animate' the static pictures solved by structural biology, by finding the forces on the atoms, and hence solving for their dynamics. But we can also move beyond this, and use rigorous thermodynamics to predict the effects that changes in structures of potential drug molecules will have on binding to their target. Such approaches were employed by computational researchers all over the world during the COVID-19 pandemic to urgently provide new understanding of the SARS-CoV-2 virus and to design inhibitors of its function.Although we know that the forces on the atoms should be calculated using the equations of quantum mechanics, these are much too computationally costly to solve routinely for biological problems. Instead the dynamics and interactions of biological molecules are typically computed using a simplified computational model, known as a force field. The force field models the atoms as bonded by springs, and interacting with each other through electrostatic and van der Waals forces. The strengths of these interactions are modelled by thousands of adjustable parameters, which have been tuned to reproduce experimental data. These force fields are an important enabling technology for biomolecular simulation scientists, and the accuracy of their predictions depends on the realism of the force field model and its parameters.Traditionally, force field models evolved over periods of many decades. Design decisions taken early in the process became 'baked in', since re-training the model with new design rules was infeasible. The Open Force Field Initiative is an academic-industrial partnership aiming to advance the science and software infrastructure required to build the next generation of molecular mechanics force fields. In one example of our work from the first period of the Fellowship, we have co-developed a flexible framework to extend the Open Force Field software stack with custom force field models. In a proof-of-principle, we were able to train and test a new generalised force field model in a matter of weeks, rather than years, with improvements in accuracy over traditional force fields.My vision for the renewal period of the Future Leaders Fellowship is to deploy this software infrastructure to rapidly move from new hypotheses to trained force field models, with unambiguous determination of the effects of design decisions on model accuracy. For example, I will test whether machine learning models trained on high-level quantum mechanical datasets yield accurate force field atomic charges, and whether accurate protein force field models can be built using the new force field models described above. Force field models that show requisite accuracy will be deployed in molecular design workflows. Through working with the project partners in the pharmaceutical industry and at an open science antiviral discovery initiative, I will showcase the accuracy improvements in structure-based biomolecular simulations that will translate to improved efficiency of the drug discovery pipeline.

数十年来，药物发现的效率一直在下降，以至于对于到达消费者的每个新分子，估计的研发成本都超过20亿美元。药物发现过程涉及通常是一个小的有机分子的设计，该分子能够以治疗益处结合其体内靶标。问题的一部分是，在这条管道中，在实验室中合成了太多的分子，以巨大的费用，这些分子并没有具有所需的结合亲和力。需要一种能够可靠地预测结构结合的计算模型。与结构生物学方法（例如X射线晶体学和冷冻电子显微镜）一样，基于计算结构的生物分子模拟是解决方案的重要组成部分。在最简单的层面上，可以使用生物分子模拟来“动画”结构生物学求解的静态图片，通过在原子上找到力，从而解决其动力学。但是我们也可以超越这一点，并使用严格的热力学来预测潜在药物分子结构的变化对与其靶标结合的影响。这种方法是由世界各地的计算研究人员在Covid-19大流行期间采用的，以紧急提供对SARS-COV-2病毒的新了解，并设计其功能的抑制剂。尽管我们知道原子上的力量应使用量子力学方程来计算这些原子方程，但这些原子方程对于求解了这些量子的成本太高，以解决常规的生物学问题。取而代之的是，通常使用简化的计算模型（称为力场）计算生物分子的动力学和相互作用。力场将原子模拟由弹簧键合，并通过静电和范德华力相互相互作用。这些相互作用的优势是由数千个可调节参数建模的，这些参数已调整为重现实验数据。这些力场是生物分子模拟科学家的重要启示技术，其预测的准确性取决于力场模型及其参数的现实主义。在传统上，力场模型在数十年的时间内演变而成。在此过程早期做出的设计决定变得“烘烤”，因为使用新的设计规则重新训练该模型是不可行的。开放部队的倡议是一种学术工业合作伙伴关系，旨在促进建立下一代分子力学力量领域所需的科学和软件基础设施。在团契第一阶段以来我们的工作的一个例子中，我们共同开发了一个灵活的框架，以使用自定义强制场模型扩展开放力量现场软件堆栈。在原始证明中，我们能够在几周而不是数年的时间内训练和测试新的广义力场模型，并且对传统势力领域的准确性提高。我对未来领导者的续签期间的愿景是部署该软件基础架构，以快速从新的假设现场模型转变为未经启动的效果效果的效果效果的效果，从而迅速转移到了新的假设现场模型中。例如，我将测试是否在高级量子机械数据集上训练的机器学习模型是否会产生准确的力场原子电荷，以及是否可以使用上述新的力场模型来构建准确的蛋白质力场模型。显示必要准确性的力场模型将在分子设计工作流程中部署。通过与制药行业的项目合作伙伴合作，并在开放的科学抗病毒发现计划中，我将展示基于结构的生物分子模拟的准确性改进，这些模拟将转化为提高药物发现管道的效率。