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Deep Learning Approaches to Improve the Efficiency of Drug Discovery

提高药物发现效率的深度学习方法

基本信息

批准号：
2105209
负责人：
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：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2105209
关键词：
Deep Learning Approaches Improve Efficiency

项目摘要

In this project, we will explore the drug discovery problem using modern statistical techniques and deep learning approaches. Focus will be placed on both improving the decision making of chemists in initial hit identification and hit-to-lead optimisation, and on developing the capabilities of automated decision making in drug design.There is currently limited literature applying machine learning to drug design and research to-date has been largely focused on analysis of 2D ligands and string representations of molecules. While these approaches have shown some success, they omit crucial structural and 3D information that are essential to protein-based interactions. Where machine learning approaches have been applied to structure-based drug discovery, they have typically not learnt features in an end-to-end fashion and have utilised pre-determined descriptors. So far in this project, we have developed an approach based on convolutional neural networks that achieved substantial improvement on popular virtual screening benchmarks. Our method treated virtual screening as a computer vision problem and used a minimally featurised input format. The model was thus forced to learn the features relevant for binding. Our analysis highlighted that more data is required to fully utilise the power of CNNs in this setting. As such, we are currently curating an expanded dataset using publicly available databases.We hope that further analysis and experiments will allow us to glean insights into key fundamental properties of protein interactions, such as binding modes, interaction types etc., while also validating the suitability of a machine learning approach to areas beyond the current literature. In addition, we expect the project to highlight unusual, and possibly novel, features of protein-ligand interactions that could then be studied on a fundamental basis by other groups/researchers.We also plan to conduct prospective evaluation of our methods, using them to predict how untested molecules will interact with a given protein, experimentally validating the theoretical hits. One area that we plan to explore is the use of machine learning techniques for guiding fragment-based approaches. In particular, we are currently designing a system to suggest elaborations of fragment hits in a principled way.There is considerable publicly available data with which to train prediction algorithms and generative models. However, one challenge of applying machine learning approaches is that while the datasets are large overall, for a given protein there is much more limited data. Thus, successful methods will need either to train efficiently on small datasets, or to be able to utilise data from protein interactions not involving the target protein. This appears feasible, but is not without complications. As a result, we aim to develop novel machine learning techniques to combat these challenges.

在这个项目中，我们将使用现代统计技术和深度学习方法探索药物发现问题。重点将放在两个提高决策的化学家在初始命中识别和命中到铅的优化，并在开发自动决策的能力，在药物design.There是目前有限的文献将机器学习应用于药物设计和研究迄今已主要集中在分析的二维配体和字符串表示的分子。虽然这些方法已经取得了一些成功，但它们忽略了对基于蛋白质的相互作用至关重要的关键结构和3D信息。当机器学习方法被应用于基于结构的药物发现时，它们通常不会以端到端的方式学习特征，而是利用预定的描述符。到目前为止，在这个项目中，我们已经开发了一种基于卷积神经网络的方法，在流行的虚拟筛选基准上取得了实质性的改进。我们的方法将虚拟筛选视为计算机视觉问题，并使用最小特征化的输入格式。因此，模型被迫学习与绑定相关的特征。我们的分析强调，在这种情况下，需要更多的数据来充分利用CNN的力量。因此，我们目前正在使用公开的数据库管理一个扩展的数据集。我们希望进一步的分析和实验将使我们能够深入了解蛋白质相互作用的关键基本特性，例如结合模式，相互作用类型等，同时也验证了机器学习方法对当前文献之外的领域的适用性。此外，我们希望该项目突出蛋白质-配体相互作用的不寻常的，可能是新的，然后可以由其他小组/研究人员在基础上进行研究的功能。我们还计划对我们的方法进行前瞻性评估，使用它们来预测未经测试的分子将如何与给定的蛋白质相互作用，实验验证理论命中。我们计划探索的一个领域是使用机器学习技术来指导基于片段的方法。特别是，我们目前正在设计一个系统，以有原则的方式建议片段命中的详细说明。有大量公开可用的数据可用于训练预测算法和生成模型。然而，应用机器学习方法的一个挑战是，虽然数据集总体上很大，但对于给定的蛋白质，数据要有限得多。因此，成功的方法需要在小数据集上进行有效的训练，或者能够利用来自不涉及靶蛋白的蛋白质相互作用的数据。这似乎是可行的，但并非没有并发症。因此，我们的目标是开发新的机器学习技术来应对这些挑战。