Development of an Ontology for Drug Screening and Design

药物筛选和设计本体论的开发

• 批准号: BB/E018025/1
• 负责人: Ross King
• 金额: $ 12.43万
• 依托单位: Aberystwyth University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2007
• 资助国家: 英国
• 起止时间: 2007 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FE018025%2F1
• 关键词: Development; Ontology; Drug; Screening; Design

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.

随着科学的进步，它产生了越来越多的数据，在某些领域，数据的增加已经成为一种洪水。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，只有当计算机处理的数据被形式化时，计算机的全部能力才能被有效地利用，也就是说，把数据的含义以一种对计算机明确的形式呈现出来。形式化知识的第一步是定义一个本体论，即描述存在的东西。本体论在生物学中越来越多地用于描述实验，因为这使得实验结果更容易被其他科学家使用，并且实验更容易重复，以确保已发表的结果是正确的。我们开发了EXPO，第一个科学实验的通用本体。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中复制它们的需要。药物设计实验可以说是英国实施的最重要的实验类型，因为它们有可能找到治疗疾病的方法：这既帮助了英国国家医疗服务体系（NHS），又为英国一些最大的公司赚钱。我们建议建立一个药物设计实验的本体。这将有助于这些实验的标准化和正式化，并将使它们的结果更容易交换和重复。本体将包括化学品本身的信息，用于测试新化学品的分析，模型，所使用的设备等。形成本体的一个主要技术困难是从专家那里提取必要的信息，因为他们发现很难表达他们的专业知识。在阿伯里斯特威斯开发的解决这个问题的一个激进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循环。在机器人科学家中，科学实验的所有方面都是明确的。在阿伯里斯特威斯，我们正在开发一个专门用于药物设计的机器人科学家，我们将用它来帮助设计药物设计本体。我们还将广泛咨询制药行业，以确保本体论捕获药物设计中的关键概念。

项目成果

Predicting rice phenotypes with meta and multi-target learning

• DOI: 10.1007/s10994-020-05881-9
• 发表时间: 2020-08
• 期刊: Machine Learning
• 影响因子: 7.5
• 作者: Oghenejokpeme I. Orhobor;N. Alexandrov;R. King

Transformational machine learning: Learning how to learn from many related scientific problems.

• DOI: 10.1073/pnas.2108013118
• 发表时间: 2021-12-07
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1
• 作者: Olier I;Orhobor OI;Dash T;Davis AM;Soldatova LN;Vanschoren J;King RD
• 通讯作者: King RD

NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding.

• DOI: 10.1038/s41540-021-00200-x
• 发表时间: 2021-10-20
• 期刊: NPJ systems biology and applications
• 影响因子: 4
• 作者: Wang K;Stevens R;Alachram H;Li Y;Soldatova L;King R;Ananiadou S;Schoene AM;Li M;Christopoulou F;Ambite JL;Matthew J;Garg S;Hermjakob U;Marcu D;Sheng E;Beißbarth T;Wingender E;Galstyan A;Gao X;Chambers B;Pan W;Khomtchouk BB;Evans JA;Rzhetsky A
• 通讯作者: Rzhetsky A