Learning to learn how to design drugs

学习如何设计药物

• 批准号: EP/K030469/1
• 负责人: Ross King
• 金额: $ 51.15万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FK030469%2F1
• 关键词: Learning; learn; how; design; drugs

A key step in developing a new drug is to learn quantitative structure activity relationships (QSARs). These are mathematical functions that predict how well chemical compounds will act as drugs. QSARs are used to guide the synthesis of new drugs.The current situation is:1) There is a vast range of approaches to learning QSARs.2) It is clear from theory and practice that the best QSAR approach depends on the type of problem.3) Currently the QSAR scientist has little to guide her/him on which QSAR approach to choose for a specific problem. We therefore propose to make a step-change in QSAR research. We will utilise newly available public domain chemoinformatic databases, and in-house datasets, to systematically run extensive comparative QSAR experiments. We will then generalise these results to learn which target-type/ compound-type/ compound-representation /learning-method combinations work best together. We do not propose to develop any new QSAR method. Rather, we will learn how to better apply existing QSAR methods. This approach is called "meta-learning", using machine learning to learn about QSAR leaning. We will make the knowledge we learn publically available to guide and improve future QSAR learning.

开发新药的关键步骤是了解定量结构活性关系（QSAR）。这些是数学函数，可以预测化合物作为药物的效果。QSAR用于指导新药的合成，目前的研究现状是：1）QSAR的学习方法多种多样; 2）从理论和实践上看，最佳的QSAR方法取决于问题的类型; 3）目前，QSAR科学家对具体问题选择哪种QSAR方法没有什么指导。因此，我们建议在QSAR研究中进行逐步改变。我们将利用新的公共领域化学信息学数据库和内部数据集，系统地运行广泛的比较QSAR实验。然后，我们将概括这些结果，以了解哪种目标类型/复合类型/复合表示/学习方法组合最好一起工作。我们不建议开发任何新的QSAR方法。相反，我们将学习如何更好地应用现有的QSAR方法。这种方法被称为“元学习”，使用机器学习来学习QSAR学习。我们将使我们学到的知识可用于指导和改善未来的QSAR学习。

项目成果

Yeast-based automated high-throughput screens to identify anti-parasitic lead compounds.

• DOI: 10.1098/rsob.120158
• 发表时间: 2013-02-27
• 期刊: Open biology
• 影响因子: 5.8
• 作者: Bilsland E;Sparkes A;Williams K;Moss HJ;de Clare M;Pir P;Rowland J;Aubrey W;Pateman R;Young M;Carrington M;King RD;Oliver SG
• 通讯作者: Oliver SG

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

Multi-task learning with a natural metric for quantitative structure activity relationship learning.

具有自然度量的多任务学习，用于定量结构活动关系学习。

• DOI: 10.1186/s13321-019-0392-1
• 发表时间: 2019
• 期刊: Journal of cheminformatics
• 影响因子: 8.6
• 作者: Sadawi N

Generic ontology of datatypes

• DOI: 10.1016/j.ins.2015.08.006
• 发表时间: 2016-02-01
• 期刊: INFORMATION SCIENCES
• 影响因子: 8.1
• 作者: Panov, Pance;Soldatova, Larisa N.;Dzeroski, Saso
• 通讯作者: Dzeroski, Saso