Applications of Deep Learning for Binding Affinity Prediction

深度学习在结合亲和力预测中的应用

• 批准号: 2887848
• 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: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2887848
• 关键词: Applications; Deep; Learning; Binding; Affinity

Background:By applying computational methods to biochemistry and developing a deeper understanding of biological systems, the efficiency of the drug discovery process, and consequently patient outcomes, could be improved. A core issue in computational chemical biology is predicting whether small molecules will bind to a protein target, reproducing the accuracy observed in experimental binding assays. In recent years, machine learning has provided substantial improvements in protein-ligand binding prediction; however, current predictors still leave much to be desired. Novel artificial intelligence methods, such as deep learning, have the potential to transform healthcare by solving issues like binding affinity prediction.Aims and objectives:To improve the generalisability of machine learning-based computational chemistry tools for predicting protein-ligand binding affinity.To explore various molecular representations with the aim of understanding how best to interpret biological systems computationally, in particular protein-ligand binding.To investigate the applicability of novel AI methods to drug discovery on protein targets, such as hERG.Novelty of the research methods:We propose investigating novel applications of deep learning, including Graph Neural Networks, Atom Centred Symmetry Functions, and Large Language Models to the fundamental problem of protein-ligand binding affinity prediction. We will assess the ability of models to learn the underlying physics, including investigating improved benchmarks, of binding interactions and explore key aspects of generalisability and non-additivity. Model applications will include predicting off-target binding to proteins involved in the absorption, distribution, metabolism, and excretion of small molecule drugs, such as hERG and cytochrome P450s, as well as other targets of interest to the partner(s). Various molecular representations and approaches will be investigated, including proteo-chemometric 1D (SMILES for small molecules; amino acid sequences for proteins), 2D (graphs for ligands, proteins, and their interactions), as well as 3D (equivariant neural networks).Alignment to EPSRC's strategies and research areas:This multidisciplinary project aligns with EPSRC's strategic delivery plan by nurturing industry and university partnerships for effective research within the UK; pursuing discovery research within the Physical and Mathematical Sciences; and targeting mission-inspired research to transform Health and Healthcare with AI. This interdisciplinary bioinformatics project spans multiple research areas within the EPSRC remit. This project falls within the EPSRC "Artificial intelligence technologies", "Biological informatics", "Chemical biology and biological chemistry", "Computational and Theoretical Chemistry" research areas.

背景：通过将计算方法应用于生物化学并对生物系统进行更深入的了解，可以提高药物发现过程的效率，从而改善患者的预后。计算化学生物学的一个核心问题是预测小分子是否会与蛋白质靶点结合，重现实验结合测定中观察到的准确性。近年来，机器学习在蛋白质-配体结合预测方面提供了实质性的改进;然而，目前的预测器仍然有很多需要改进的地方。新的人工智能方法，如深度学习，有可能通过解决结合亲和力预测等问题来改变医疗保健。目的和目标：提高基于机器学习的计算化学工具的通用性，用于预测蛋白质-配体结合亲和力。探索各种分子表示，旨在了解如何最好地解释生物系统的计算，特别是蛋白质-配体结合。研究新的人工智能方法对蛋白质靶点药物发现的适用性，如hERG。研究方法的新奇：我们建议研究深度学习的新应用，包括图神经网络，原子中心对称函数和大型语言模型，以解决蛋白质-配体结合亲和力预测的基本问题。我们将评估模型学习基础物理的能力，包括研究改进的基准，结合相互作用，并探索概括性和非加性的关键方面。模型应用将包括预测与小分子药物（如hERG和细胞色素P450）的吸收、分布、代谢和排泄相关蛋白质以及伴侣感兴趣的其他靶标的脱靶结合。将研究各种分子表示和方法，包括蛋白质化学计量学1D（小分子的SMILES;蛋白质的氨基酸序列），2D（配体，蛋白质及其相互作用的图表），以及3D与EPSRC的战略和研究领域保持一致：这个多学科项目通过培养行业和大学的伙伴关系，在英国进行有效的研究，与EPSRC的战略交付计划保持一致;在物理和数学科学领域进行发现研究;并以使命为导向的研究为目标，通过人工智能改变健康和医疗保健。这个跨学科的生物信息学项目跨越了EPSRC职权范围内的多个研究领域。该项目福尔斯EPSRC“人工智能技术”，“生物信息学”，“化学生物学和生物化学”，“计算和理论化学”研究领域。