Development of an Ontology for Drug Screening and Design

随着科学的进步，它产生了越来越大的数据量，在某些领域，这种数据的增加已经变成了洪水。其结果是，科学越来越依赖计算机来存储、整合和分析数据。然而，只有当计算机使用的数据被形式化，即以一种数据的形式向计算机表明数据的含义时，计算机的全部能力才能被有效地利用。形式化知识的第一步是定义本体论，即描述存在的东西。本体论在生物学中越来越多地被用来描述实验，因为这使得实验结果更容易被其他科学家使用，实验也更容易重复--以确保公布的结果是正确的。我们开发了第一个科学实验的通用本体论--EXPO。这收集了实验中最基本的概念，并消除了在专门科学的本体论中重复它们的需要。药物设计实验可以说是英国执行的最重要的实验类型，这要归功于它们找到治疗和治愈疾病的潜力：这既有助于NHS，也为英国一些最大的公司赚钱。我们建议开发一个药物设计实验的本体。这将有助于将这些实验标准化和正规化，并使它们的结果更容易交换和重复。本体将包括有关化学品本身、用于测试新化学品的分析、模型、使用的设备等的信息。形成本体的一个主要技术困难是从专家人类那里提取必要的信息，因为他们发现很难清楚地表达自己的经验。在Aberystwyth开发的一个解决这个问题的根本方法是使用机器人科学家。这些人工智能系统能够完全自动化科学的简单部分/假设形成、实验计划、实验执行和结果分析的周期。在机器人科学家中，科学实验的所有方面都是明确的。在Aberystwyth，我们正在开发一个专为药物设计而设计的机器人科学家，我们将使用它来帮助设计药物设计本体。我们还将广泛咨询制药业，以确保本体论涵盖药物设计中的关键概念。

As science progresses it is generating ever larger amounts of data, and in some fields this increase in data has become a deluge. The result is that science increasingly depends on computers to store, integrate, and analyse data. However, the full power of computers can only be efficiently exploited when the data they work with is formalised, i.e. put into a form where the meaning of the data is made explicit to the computer. The first step in formalising knowledge is to define an ontology, i.e. to describe what exists. Ontologies are increasingly used in biology to describe experiments, as this enables the results of the experiment to be more easily used by other scientists, and for the experiment to be more easily repeated - to ensure that the published results are correct. We have developed EXPO, the first general ontology of scientific experiments. This collects together the most basic concepts in experiments, and removes the need to duplicate them in ontologies for specialized sciences. Drug design experiments are arguably the most important type of experiments executed in the UK, thanks to their potential to find treatments and cures for diseases: which both helps the NHS, and makes money for some of the UK's largest companies. We propose to develop an ontology of drug design experiments. This will help standardize and formalise these experiments, and will make their results more easily exchanged and repeated. The ontology will include information on the chemicals themselves, the assays used to test new chemicals, the models, the equipment used, etc. A major technical difficulty in forming ontologies is to extract the necessary information out of expert humans, as they find it hard to articulate their experise. One radical way around this problem that has been developed in Aberystwyth is to use Robot Scientists. These are Artificial Intelligence systems that are capable of fully automating simple parts of science / cycles of hypothesis formation, experiment planning, experiment execution, and results analysis. In a Robot Scientist all aspects of a scientific experiment are explicit. In Aberystwyth we are developing a Robot Scientist designed for drug design, and we will use this to help design the drug design ontology. We will also consult widely with the pharmaceutical industry to ensure that the ontology captures the key concepts in drug design.