Modern statistical techniques for assessing and predicting herbicide performance

这个项目将发展图形数据的现代回归方法。随着化学测试筛选工具的普及，现在可以很容易地建立庞大的化学物质数据库。另一方面，可用于分析这些大型数据库的严格统计方法的发展尚处于起步阶段，进一步发展以促进化学发现是必不可少的。在这个项目中，我将开发统计模型和方法，从化合物的描述特征和筛选测试中的表现来评估化合物，并相应地计算每种化学物质的定量分数。我将运用这个方法来解决先正达公司提供的实际问题，使用他们除草剂筛选实验的数据。通常情况下，数千种潜在除草剂将在实验室进行一系列筛选试验（分析试验），每次都将丢弃无效化合物，并根据一套更复杂的标准对剩余化合物进行评估，最后的少数除草剂将进行严格的实地试验。显然，早期试验的数据将表现出高度的不确定性和主观性。我们与先正达共同确定了该项目不同可能发展的动机和起点。在项目的开始阶段，我将开发一个模型来预测除草剂在每次测试中的性能，使用诸如剂量、测试的植物种类和化学物质的结构等信息，这些信息可以以图表的形式呈现。现代回归方法，如支持向量回归，神经网络和高斯过程回归将被探索。该模型应该利用植物物种和化学物质家族之间的关系来提高预测性能，潜在的好处是发现新的和有效的除草剂，发展或当前的统计和机器学习技术，为未来的研究和发展提供空间。研究委员会的好处包括通过提供国际上优秀的、发现驱动的研究，帮助英国成为全球领先的研究国家，指导英国研究界应对全球挑战和政府主导的倡议，提供具有经济和社会影响的优秀研究，以满足国家的需求。

This project will develop modern regression methods for graphical data. With the proliferation of screening tools for chemical testing, it is now possible to create vast databases of chemicals easily. On the other hand, the development of rigorous statistical methodology that can be used to analyse these large databases is in its infancy, and further development to facilitate chemical discovery is imperative. In this project, I will develop statistical models and methodology for assessing chemical compounds from their descriptive characteristics and their performance on screening tests, and accordingly compute a quantitative score for each chemical. I will apply this methodology to tackle real problems provided by Syngenta using data from their screening experiments on herbicides. Typically, thousands of potential herbicides will undergo a sequence of screening tests (assay tests) in the lab and each time ineffective compounds will be discarded and the remaining are assessed against a more complex set of criteria, with the final few undergoing rigorous field trials. Evidently, the data from the early trials will exhibit high uncertainty and subjectivity. Motivation and starting points for different possible developments in this project have been identified together with Syngenta. In the starting phase of the project, I will develop a model to predict the herbicide's performance on each test using information such as dosage, plant species tested, and the chemical's structure which can be presented as a graph. Modern regression methods such as support vector regression, neural networks, and Gaussian process regression will be explored. The model should exploit the relationships between plant species and families of chemicals to improve predictive performanceThe potential benefits are discovery of new and effective herbicides, development or current statistical and machine learning techniques, room for future research and development.Benefits to the research council include helping UK become the global leading research nation by offering internationally excellent, discovery-driven research, guiding the UK research community in responding to global challenges and government-led initiatives, deliver excellent research with economic and social impact that addresses the needs of the nation.