A Robot Chemist

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，证明了其成功。现在的建议是赋予Eve进行化学反应和合成新化合物的能力。Eve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经常可以找到的设备，用于将液体分配到排在塑料盘子上的大量油井中，然后进行孵化和测试。但通过将通常分散在不同部门的仪器整合在一起，Eve可以进行测试并解释结果，并在进一步的测试中更快地继续使用这些知识。现在，我们将赋予Eve设计和制造她自己的新化合物的权力，然后再测试它们的药物发现潜力。对于今天可用的大多数药物，科学家们将药物分子视为纳米级的钥匙，插入我们体内细胞中类似大小的蛋白质或酶锁中。药物筛选测试使用生物系统来触发这些锁，当分子像钥匙一样装进锁中时，会触发信号，例如荧光灯。虽然制药行业的筛查可以识别被称为HITS的积极信号，但Eve也能够独立跟踪和检查HITS是否是真正的潜在客户，即所谓的线索。但简单地筛选和跟踪命中并不是Eve在药物发现方面最大的希望所在。取而代之的是，通过学习这些测试的结果，Eve能够完成目前化学家和生物学家团队需要数月时间才能敲定的工作。目前，药物研究人员已经使用了一种软件，该软件使用“机器学习”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(如脂肪和电学变化)与它们可能是多么好的药物联系起来。使用这样的模型，科学家们选择下一步要制造和测试的分子。目前，伊芙只能学会预测15,000种化合物中的哪一种会被击中。这项提议是为EVE增加合成新化合物的能力。特别是，我们将对EVE进行编程，使其能够执行被称为“后期功能化”的化学过程。这是将一个与药物相关的化学基团引入到伊芙图书馆中现有的类药物分子中。这将使Eve能够制造新的化学实体，并形成更多的类药物分子。我们将使用机器学习对EVE进行编程，以(1)学习如何最好地使用后期功能化设计药物，以及(2)了解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EVE开发、优化和‘道路测试’一种新的重要化学过程，这将对世界各地的分子制造商大有裨益。然而，该项目的主要目标是使药物发现更便宜、更快。这将有助于开发目前因经济原因而被忽视的疾病的治疗方法，如热带和孤儿疾病，并普遍增加新药的供应，从而有可能改善世界各地数百万人的生活。

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.