Geometric Deep Learning for Binding Affinity Prediction

用于结合亲和力预测的几何深度学习

• 批准号: 2597682
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2597682
• 关键词: Geometric; Deep; Learning; Binding; Affinity

Drug discovery is an incredibly expensive and time-consuming process. The average cost of bringing a drug to market is $1.3 billion, which is doubling every nine years, and takes ten to fifteen years. The development of machine and deep learning techniques have promised to improve the efficiency of the drug discovery process and arrest this decline in productivity. Structure-based drug discovery, one method of discovery, uses computational methods and the 3D structure of a protein to identify novel drugs that bind to the target. Accurate scoring functions that can predict the binding affinity between a protein and a small molecule drug have been developed using machine learning over the last decade. These machine learning (ML)-based scoring functions have improved accuracy over pre-existing methods. However, they have been primarily trained and validated using solved crystal structures of bound protein-ligand complexes. This does not accurately represent a real-world drug discovery scenario where crystal structures for the bound protein-ligand complexes are not available. Sometimes there may be no crystal structure of the protein at all, and modelled structures must be used. In addition, ML-based scoring functions are typically trained and validated using data from a single source and often fail to generalise to novel data sets. To discern between predictions from the scoring functions that result from a bias in the training data and those that do not, the model's reasoning must be fully understood. Current scoring functions do not provide this information and so have a reduced trust in the field to be used in drug discovery. Approaches such as attribution have shown promise in elucidating why the scoring function has made certain predictions. Attribution can be used to check whether predictions have been made based on the interactions between the protein and drug instead of just on the composition of the drug alone, a common pitfall for scoring functions. This DPhil aims to develop a binding affinity model using deep learning that has been explicitly designed to address the flaws currently in existing models in the field. Geometric deep learning architectures, such as equivariant graph neural networks, have been successfully utilised for this problem and combined with attribution to show that the scoring function does learn biomolecular interactions instead of bias to make predictions. These architectures will be built upon to extend the accuracy for modelled structures, not just experimentally determined crystal structures, to allow increased reliability for realistic applications of scoring functions. A scoring function that is accurate, reliable, and easily interpretable will be valuable in any drug discovery project and will be available for all as open-source software. The research will be completed in collaboration with the medical research charity LifeArc and their employees Dr Andy Merritt and Dr Kristian Birchall, who have valuable experience in accelerating healthcare innovation to make breakthroughs for patients. The project falls within the EPSRC AI and Data Science for Engineering, Health, and Government (ASG) research area.

药物发现是一个极其昂贵和耗时的过程。将一种药物推向市场的平均成本为13亿美元，每九年翻一番，需要十到十五年的时间。机器和深度学习技术的发展有望提高药物发现过程的效率，阻止生产率的下降。基于结构的药物发现是一种发现方法，它使用计算方法和蛋白质的3D结构来识别与靶标结合的新药。在过去的十年里，使用机器学习开发了可以预测蛋白质和小分子药物之间结合亲和力的准确评分函数。这些基于机器学习(ML)的评分函数比以前存在的方法具有更高的准确性。然而，它们已经被初步训练，并使用结合蛋白-配体复合体的已求解晶体结构进行验证。这并不能准确地代表一个现实世界的药物发现场景，在这个场景中，结合的蛋白质-配体复合体的晶体结构是不可用的。有时蛋白质可能根本没有晶体结构，必须使用模型化结构。此外，基于ML的评分函数通常是使用来自单一来源的数据来训练和验证的，并且通常不能推广到新的数据集。为了区分由于训练数据中的偏差而导致的评分函数的预测和不是这样的预测，必须完全理解模型的推理。目前的评分功能不提供这种信息，因此对用于药物发现的领域的信任度降低。归因等方法在解释为什么评分函数做出某些预测方面显示出了希望。归因可以用来检查是否已经根据蛋白质和药物之间的相互作用做出了预测，而不仅仅是药物的组成，这是评分功能的常见陷阱。这个DPhil的目标是使用深度学习开发一个绑定亲和力模型，该模型已经明确地设计来解决该领域现有模型中的缺陷。几何深度学习结构，如等变图神经网络，已被成功地用于解决这个问题，并与属性相结合，表明评分函数确实学习生物分子相互作用，而不是偏见来进行预测。这些体系结构的构建将扩展建模结构的精度，而不仅仅是实验确定的晶体结构，以便为评分函数的实际应用提供更高的可靠性。一个准确、可靠、易于解释的评分功能在任何药物发现项目中都将是有价值的，并将作为开放源码软件提供给所有人。这项研究将与医学研究慈善机构LifeArc及其员工Andy Merritt博士和Kristian Birchall博士合作完成，他们在加快医疗创新以实现患者突破方面拥有宝贵经验。该项目属于EPSRC人工智能和工程、卫生和政府数据科学(ASG)研究领域。