Deep Learning Approaches to Improve the Efficiency of Drug Discovery

在这个项目中，我们将使用现代统计技术和深度学习方法来探索药物发现问题。重点将放在改善化学家在初始命中识别和命中到领先优化中的决策能力，以及发展药物设计中的自动决策能力。目前将机器学习应用于药物设计和研究的文献有限，到目前为止，主要集中在分析2D配体和分子的字符串表示。虽然这些方法已经取得了一些成功，但它们忽略了关键的结构和3D信息，这些信息对于基于蛋白质的相互作用是必不可少的。当机器学习方法被应用于基于结构的药物发现时，它们通常没有以端到端的方式学习特征，而是利用了预先确定的描述符。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一个计算机视觉问题，并使用了最小特征的输入格式。因此，该模型被迫学习与绑定相关的功能。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可用的数据库来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属性，如结合模式、相互作用类型等，同时也验证机器学习方法在当前文献之外的领域的适用性。此外，我们预计该项目将突出蛋白质-配体相互作用的不寻常的、可能是新颖的特征，然后其他小组/研究人员可以在基础上对这些特征进行研究。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，并通过实验验证理论上的成功。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，以原则性的方式建议对片段命中的详细说明。有大量公开可用的数据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，尽管数据集总体上很大，但对于给定的蛋白质来说，数据要有限得多。因此，成功的方法要么需要在小数据集上有效训练，要么需要能够利用不涉及目标蛋白质的蛋白质相互作用的数据。这似乎是可行的，但也不是没有复杂的情况。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。

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.