A Robot Chemist

机器人化学家

• 批准号: EP/S014128/1
• 负责人: Ross King
• 金额: $ 31万
• 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: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FS014128%2F1
• 关键词: Robot; Chemist

Eve is an artificially-intelligent 'Robot Scientist' designed to make drug discovery faster and much cheaper. She has already discovered that a compound used in soap and toothpaste might also be used in the fight against drug-resistant malaria, demonstrating its success. The proposal is to now give Eve the ability to do chemical reactions and to synthesise new compounds.Eve inhabits an enclosure 2.5 meters in length, 2 metres wide, and 93 meters high. It consists of two robot arms, surrounded by equipment regularly found in laboratories for dispensing liquids into a large number of wells lined up on plastic plates, then incubating and testing them. But by integrating together instruments usually separated into different departments, Eve can do tests and interpret the results, and go on and use that knowledge in further tests faster. We will now give Eve the power to design and make her own, new compounds before testing their potential for drug discovery.For the majority of medicines available today, scientists view drug molecules as nanometre-scale keys that slot into similarly sized protein or enzyme locks in cells in our bodies. Drug screening tests put these locks using biological systems that trigger a signal, such as a fluorescent flash, when a molecule fits into it like a key. While pharmaceutical industry screening can identify positive signals known as hits, Eve is also independently able to follow up and check if the hits were true prospects, known as leads. But simply screening and following up hits is not where Eve's greatest promise for drug discovery lies. Instead, by learning from the results from those tests, Eve is able to do what it currently takes teams of chemists and biologists many months to hammer out. Drug researchers currently already use software that employs 'machine learning' to take screening results and create a 'quantitative structure-activity relationship'. This is a mathematical function that relates the composition, shape and properties like fattiness and electrical change of the molecules, to how good drugs they are likely to be. Using such models scientists choose which molecule to make and test next. Currently, Eve can only learn to predict which out of a large set of ~15,000 compounds would be hits. The proposal is to add to Eve the ability to also synthesise novel compounds. In particular, we will program Eve to be able to carry out a chemical process known as 'late stage functionalization'. This is the introduction of a medicinally-relevant chemical group to existing drug-like molecules in Eve's library. This will enable Eve to make new chemical entities and to form an extended collection of drug-like molecules. We will program Eve to use machine learning to (1) Learn how to best to design drugs using late stage functionalization, and (2) learn which molecules are most likely to undergo successful late stage functionalization.An important goal of our project, therefore, is for Eve to develop, optimize and 'road test' a new and important chemical process that will be of great use to molecule-makers around the world. However, the main project goal is to make drug discovery cheaper and faster. This will enable the development of treatments for diseases currently neglected for economic reasons, such as tropical and orphan diseases, and generally increase the supply of new drugs, and so potentially improve the lives of millions of people worldwide.

Eve是一个人工智能的“机器人科学家”，旨在使药物发现更快，更便宜。她已经发现，一种用于肥皂和牙膏的化合物也可能用于对抗抗药性疟疾，这证明了它的成功。他们的计划是让夏娃有能力进行化学反应并合成新的化合物。夏娃居住在一个长2.5米、宽2米、高93米的围栏里。它由两个机械臂组成，周围环绕着实验室中常见的设备，用于将液体分配到塑料板上排列的大量威尔斯孔中，然后对其进行孵育和测试。但是，通过将通常分散在不同部门的仪器集成在一起，Eve可以进行测试并解释结果，并继续在进一步的测试中更快地使用这些知识。我们现在将赋予Eve设计和制造自己的新化合物的权力，然后再测试它们在药物发现方面的潜力。对于当今可用的大多数药物，科学家将药物分子视为纳米级的钥匙，可以插入类似大小的蛋白质或酶中。锁在我们体内的细胞中。药物筛选测试使用生物系统将这些锁放入其中，当一个分子像钥匙一样插入其中时，生物系统会触发一个信号，例如荧光闪烁。虽然制药行业的筛选可以识别出被称为命中的积极信号，但Eve也能够独立跟进并检查命中是否是真正的前景，称为线索。但是，简单地筛选和跟踪命中并不是夏娃发现药物的最大希望所在。相反，通过从这些测试的结果中学习，Eve能够做到目前需要化学家和生物学家团队数月才能完成的事情。目前，药物研究人员已经使用采用“机器学习”的软件来获取筛选结果并创建“定量结构-活性关系”。这是一个数学函数，它将分子的组成、形状和性质（如脂肪含量和电变化）与它们可能成为多好的药物联系起来。利用这种模型，科学家们选择下一步要制造和测试的分子。目前，Eve只能学会预测大约15，000种化合物中的哪一种会被击中。这项提议是为了给夏娃增加合成新化合物的能力。特别是，我们将对Eve进行编程，使其能够进行一种称为“后期功能化”的化学过程。这是在Eve的库中现有的药物样分子中引入了一个医学相关的化学基团。这将使夏娃能够制造新的化学实体，并形成一个扩展的药物样分子集合。我们将对Eve进行编程，使其使用机器学习来（1）学习如何最好地使用后期功能化设计药物，（2）学习哪些分子最有可能经历成功的后期功能化。因此，我们项目的一个重要目标是让Eve开发、优化和“路测”一种新的重要化学工艺，这将对世界各地的分子制造商非常有用。然而，该项目的主要目标是使药物发现更便宜，更快。这将有助于开发治疗目前因经济原因而被忽视的疾病的方法，如热带病和孤儿病，并普遍增加新药的供应，从而有可能改善全世界数百万人的生活。

项目成果

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

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

Trifluoromethyl Sulfoxides: Reagents for Metal-Free C-H Trifluoromethylthiolation.

• DOI: 10.1002/anie.202005531
• 发表时间: 2020-09-07
• 期刊: Angewandte Chemie (International ed. in English)
• 作者: Wang D;Carlton CG;Tayu M;McDouall JJW;Perry GJP;Procter DJ
• 通讯作者: Procter DJ