Novel Data Science and Mathematical Approaches to Drug Discovery

将一种新药推向市场的过程既漫长又昂贵，估计成本约为26亿美元，平均时间为10-15年。新药的开发目前受到临床试验的高失败率、未知的靶结构以及使用资源密集型高通量筛选(HTS)来识别活性分子的限制。计算机辅助药物发现(CADD)为药物分子识别以及对潜在副作用和生物活性的预测提供了一条虚拟的捷径，与高通量筛选相比，减少了所需的时间和财力。CADD中使用的一种方法是利用一组已知与目标相互作用的配体作为参考，并将这些配体的形状与其他分子进行比较。这种方法利用了相似性质原理：结构相似的分子将表现出相似的性质，因此很可能与相同的蛋白质靶标相互作用。为了能够进行这种比较，可以用许多方法来描述配体的形状，包括基于原子之间的距离、高斯球的体积重叠和基于分子表面的描述。然而，这一领域的现有方法在选择正确的查询分子、结构的可视化以及在某些情况下可以比较结构的速度方面提出了挑战。本项目旨在通过开发一种新的基于其分子表面的配体形状描述来解决这些问题。这将通过考虑一种描述表面几何的数学方法来实现，在该方法中，计算特定分子的量化Kähler势(从微分几何和超弦理论领域得出的概念)。这种潜力可以量身定做，以平衡精度和速度。这为每个分子产生了一组系数，这些系数可以通过相似性度量(例如欧几里德距离、曼哈顿距离或塔尼托系数，所有这些都是形状相似性方法中常用的)来比较两个结构。在用定义明确的活性和非活性分子(例如G蛋白偶联受体和EGFR激酶抑制剂)对蛋白质靶标进行测试之前，将产生该形状描述符的蟒蛇编码版本。然后，新方法将被应用于数据库的虚拟筛选，并用作机器学习模型的输入。还将完成基准研究，以将我们的新描述符与上述现有方法进行比较。如果成功，该项目的最终目标是将新的描述符应用于纽卡斯尔大学的药物发现工作。

The process of bringing a new drug to market is lengthy and expensive, with estimated costs of around $2.6 billion and an average timescale of 10-15 years. The development of novel drugs is currently limited by high failure rates during clinical trials, unknown target structures and the use of resource-intensive high-throughput screening (HTS) to identify active molecules. Computer-aided drug discovery (CADD) provides a "virtual shortcut" for drug molecule identification, along with the prediction of potential side effects and biological activity, reducing the time and financial resources required compared to high-throughput screening. One approach used in CADD is to make use of a set of ligands known to interact with a target as a reference, and to compare the shape of these to other molecules. This method makes use of the Similar Property Principle: structurally similar molecules will display similar properties, and therefore are likely to interact with the same protein target. To enable this comparison the shape of the ligand can be described in many ways, including descriptions based on the distances between atoms, volume overlap of Gaussian spheres and those based on the molecular surface. However existing methods in this field present challenges in the selection of the correct query molecule, visualisation of the structure and in some cases with the speed at which the structures can be compared. This project aims to address these issues with the development of a novel description of ligand shape based on its molecular surface. This will be achieved by considering a mathematical approach to the description of the geometry of a surface, in which the quantised Kähler potential (a concept drawn from the fields of differential geometry and superstring theory) for a particular molecule is computed. The potential can be tailored to balance accuracy with speed. This produces a set of coefficients for each molecule, which can be used to compare two structures through a similarity metric (for example the Euclidean Distance, the Manhattan Distance or the Tanimoto Coefficient, all of which are commonly used in shape similarity approaches). A python-coded version of this shape descriptor will be produced, before testing on protein targets with well-defined sets of active and inactive molecules (e.g. G-Protein Coupled Receptors and EGFR kinase inhibitors). The new method will then be applied in virtual screening of databases and used as input to machine learning models. Benchmarking studies will also be completed to compare our new descriptor to the existing methods outlined above. If successful, the ultimate aim for the project is to apply the new descriptor to drug discovery efforts at Newcastle University.