Integrating computational chemistry models of ion channels to build a holistic cardiac safety prediction tool for drug discovery

整合离子通道的计算化学模型，构建用于药物发现的整体心脏安全预测工具

• 批准号: 2779681
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2779681
• 关键词: Integrating; computational; chemistry; models; ion

Ensuring that any potential new medicine does not cause harmful arrhythmias has long been a great challenge. The major cause of this is the ability of many compounds to interfere with ion channels that are crucial to the heart's regular beat. In this project computational chemistry tools will be combined with cutting edge machine learning approaches to build a holistic model for cardiac safety that will be of great benefit to drug discovery. The aspiration is that a scientist could draw a chemical structure that would then be placed inside each of the most important cardiac ion channels. Quantum mechanical calculations would provide the binding energy between the molecule and each of the ion channels. Finally, the computed binding energies with all of the key ion channels would be integrated to provide a prediction of the in vivo activity of the compound. The current alternative to this approach is to test the compound using animals; not only could the computational approach avoid many of these animal tests, it would be superior to them because it provides an explanation for any problems and can be used to design alternative, safe molecules. The multidisciplinary team that will be supporting the project includes computational and synthetic chemists, biologists and even clinicians working in the adjacent hospitals providing cardiovascular care. The project will see the student first build a few models of individual ion channels and validate these with all of the known compound binding data. They will then select a set of compounds with known cardiac safety and compute their binding energy in all of their own computational models as well as all others available in the group at that point in time. This will provide a computed profile of the compounds for many ion channels. The link between these profiles and the known in vivo activity of the compounds will be explored using the latest machine learning techniques. These techniques are currently of high interest across the pharmaceutical and other industrial sectors. Subsequently, the student would design the last part of the project themselves with possibilities including making compounds to test their own predictions, testing compounds in in vitro assays or applying the machine learning to other therapeutic areas, such as neuroscience, that also have links to ion channel activity. This project is tailored to address two of the key MRC themes, namely Quantitative Skills and Interdisciplinary Skills. Within the quantitative skills, the project will train the student in ALL of the listed areas (mathematics, statistics, computation, data analytics and informatics, machine learning and Artificial Intelligence, developing digital and technology excellence) in this case, the domain of application will be to whole organism and whole tissue findings deriving from in vivo experiments (including clinical findings) and to a range of in vitro experimental findings. The project team and project outline reflect the high interdisciplinarity of the project that will require the student to work at the interface where chemical, physical and computational science meet in vivo biology and clinical application. The computational methods involved will see the underlying physics of molecular interactions described accurately in a way that will provide chemical insight. The student will then apply the machine learning techniques to forge a link from the molecular level to the whole organism/tissue level.

确保任何潜在的新药不会导致有害的心律失常一直是一个巨大的挑战。造成这种情况的主要原因是许多化合物干扰离子通道的能力，而离子通道对心脏的正常跳动至关重要。在这个项目中，计算化学工具将与尖端的机器学习方法相结合，建立一个心脏安全的整体模型，这将对药物发现大有裨益。科学家们的愿望是绘制出一个化学结构，然后将其放置在每个最重要的心脏离子通道中。量子力学计算将提供分子和每个离子通道之间的结合能。最后，将计算出的与所有关键离子通道的结合能积分，以提供化合物的体内活性的预测。目前这种方法的替代方案是使用动物来测试化合物;计算方法不仅可以避免许多这些动物测试，而且还上级它们，因为它为任何问题提供了解释，并可用于设计替代的安全分子。支持该项目的多学科团队包括计算和合成化学家，生物学家，甚至是在附近医院提供心血管护理的临床医生。该项目将看到学生首先建立几个模型的个别离子通道和验证这些与所有已知的化合物结合数据。然后，他们将选择一组具有已知心脏安全性的化合物，并在他们自己的所有计算模型中计算它们的结合能，以及该组中在该时间点可用的所有其他模型。这将为许多离子通道提供化合物的计算概况。将使用最新的机器学习技术探索这些特征与化合物已知的体内活性之间的联系。这些技术目前在制药和其他工业部门受到高度关注。随后，学生将自己设计项目的最后一部分，包括制造化合物来测试自己的预测，在体外试验中测试化合物或将机器学习应用于其他治疗领域，如神经科学，这些领域也与离子通道活性有关。这个项目是专门针对两个关键的MRC主题，即定量技能和跨学科技能。在定量技能中，该项目将在所有列出的领域培训学生（数学，统计学，计算，数据分析和信息学，机器学习和人工智能，发展数字和技术卓越）在这种情况下，应用领域将是从体内实验得到的整个生物体和整个组织的发现（包括临床发现）和一系列体外实验发现。项目团队和项目大纲反映了该项目的高度跨学科性，这将要求学生在化学，物理和计算科学满足体内生物学和临床应用的界面上工作。所涉及的计算方法将看到分子相互作用的基本物理学以一种提供化学见解的方式准确描述。然后，学生将应用机器学习技术建立从分子水平到整个生物体/组织水平的联系。