Antimicrobial resistance to existing antibiotics is a growing crisis. Not only are new antibiotics exceptionally costly to develop, but they are usually limited to a narrow spectrum of chemical diversity. In January, the Director-General of the World Health Organisation declared:

对现有抗生素的一个 ntimicrobial耐药性是一个日益严重的危机 。 新抗生素不仅开发成本高昂，而且通常仅限于狭窄的化学多样性范围。 一月，世界卫生组织总干事宣布 ：

“Never has the threat of antimicrobial resistance been more immediate and the need for solutions more urgent.”

“从未出现过抗药性的威胁，而且迫切需要解决方案。”

The process of bringing new medicines from the bench to the bedside, known as drug discovery, is expensive, difficult and sometimes inefficient. It has been estimated that the average drug discovery project takes over a decade and costs around $3 billion. This is not just the case for new antibiotics but also the search for other new drugs, such as an effective antiviral treatment for SARS-CoV-2, the virus that causes COVID-19.

将新药从实验台带到床边的过程称为药物发现，该过程昂贵，困难且有时效率低下。 据估计，平均每个药物发现项目需要十年时间， 耗资约30亿美元 。 这不仅是新抗生素的情况，还包括寻找其他新药，例如有效治疗 SARS-CoV-2(引起COVID-19的病毒)的抗病毒药物 。

But, earlier this year, a team from MIT and Harvard made headlines when they announced that they had discovered a powerful new antibiotic using artificial intelligence (AI). Deep neural networks had identified an existing drug, halicin, that was structurally divergent from existing antibiotics and exhibited potent bactericidal activity against a wide spectrum of pathogens.

但是，在今年早些时候，麻省理工学院和哈佛大学的一个团队登上新闻头条 ，他们宣布他们已经利用人工智能(AI)发现了一种功能强大的新型抗生素 。 深度神经网络已经发现了一种现有的药物，halicin，其结构与现有抗生素有所不同，并且具有针对多种病原体的有效杀菌活性。

Today, AI is a ubiquitous feature of modern life and is responsible for the “smart” technology behind email spam filters, social media feeds and plagiarism checkers. More advanced forms of AI, such as machine learning and deep learning, solve complex problems by breaking them down into layers of data, similar to the neurons in our brains.

如今，人工智能已成为现代生活中无处不在的特征，它负责电子邮件垃圾邮件过滤器，社交媒体源和窃检查器背后的“智能”技术。 机器学习和深度学习等更高级的AI形式通过将复杂的问题分解为数据层来解决复杂的问题，类似于我们大脑中的神经元。

Consequently, it is hoped that integrating AI methods with traditional drug discovery will improve time- and cost-efficiency, accelerating the transition of new drugs from the laboratory to the shelf. Here are five exciting developments at the interface of medicine and machine.

因此，希望将AI方法与传统药物发现相结合将改善时间和成本效率，加快新药从实验室到货架的过渡。 这是医学与机器之间的五个激动人心的发展。

Classifying and sorting cells by image analysis

通过图像分析对细胞进行分类和排序

A drug discovery project usually begins by identifying a disease area and a target related to that disease, such as a metabolic pathway or a protein. In the case of an infectious disease, such as malaria or tuberculosis, it is important that the pathway or protein is unique to the disease-causing organism to minimise potential interactions with the cells of the host (the patient).

药物发现项目通常从识别疾病区域和与该疾病相关的靶标(例如代谢途径或蛋白质)开始。 在诸如疟疾或结核病等传染性疾病的情况下，重要的是，该途径或蛋白质对于致病生物而言是独特的，以最大程度减少与宿主细胞(患者)的潜在相互作用。

Typically, suitable compounds are identified from vast molecular libraries using methods such as high-throughput screening. Visual inspection of these data is a tedious task and quickly becomes inefficient for the analysis of big data sets. Fortunately, AI is an excellent tool for recognising images and can be trained to rapidly classify and even sort various cell types.

通常，使用诸如高通量筛选的方法从庞大的分子库中识别合适的化合物。 这些数据的视觉检查是一项繁琐的任务，并且对于大数据集的分析很快变得效率低下。 幸运的是， 人工智能是识别图像的出色工具 ，可以接受训练以快速对各种细胞类型进行分类甚至分类。

These algorithms are also finding application in the interpretation of screening mammograms for breast cancer, in which an AI system successfully reduced the number of false positive and false negative cases and, in an independent study, outperformed six human radiologists.

这些算法也可用于解释乳腺X光检查的解释中 ，其中AI系统成功地减少了假阳性和假阴性病例的数量，并且在一项独立研究中，其表现优于六名人类放射科医生。

Predicting the three-dimensional structure of a target protein

预测 靶蛋白 的三维 结构

Understanding the target protein is a crucial piece of the drug discovery puzzle. If the binding pocket of a target protein is well understood, new molecules can then be designed to fit into this pocket, analogous to the way in which a key (drug) precisely fits into a lock (protein).

了解靶蛋白是药物发现难题的关键。 如果很好地了解了目标蛋白的结合口袋，则可以设计新分子使其适合该口袋，这类似于钥匙(药物)精确地适合锁(蛋白质)的方式。

Predicting the three-dimensional structure of a protein is notoriously challenging but, with the development of AI-based tools, these calculations have become more accurate and sophisticated. For example, a tool called AlphaFold relies on deep neutral networks trained to predict the distances between pairs of amino acid residues. In a blind competition, AlphaFold was able to predict the three-dimensional structure of a target protein 24 of 43 times. This was significantly better than the runner-up software, which correctly predicted only 14 out of 43 test sequences.

众所周知，预测蛋白质的三维结构具有挑战性，但是随着基于AI的工具的发展，这些计算变得更加准确和复杂。 例如， 一种名为AlphaFold的工具依赖于经过深化的中性网络，这些网络经过训练可以预测成对的氨基酸残基之间的距离。 在盲目竞争中，AlphaFold能够预测目标蛋白质的24次三维结构，共43次。 这明显优于亚军软件，后者可以正确预测43个测试序列中的14个。

Predicting physical properties

预测物理性质

Some molecules are more “drug-like” than others, which means that they are more likely to be absorbed by tissues or are less likely to be metabolised by liver enzymes. These parameters are often linked to the physical properties of the molecule, such as its melting point or its partition coefficient. Computers are getting better at predicting these physical properties, saving precious time in the laboratory.

一些分子比其他分子更像“药物样”，这意味着它们更容易被组织吸收或不太可能被肝酶代谢。 这些参数通常与分子的物理性质有关，例如其熔点或分配系数。 计算机在预测这些物理特性方面越来越好，从而节省了宝贵的实验室时间。

The toxicological profile of a compound is also an important parameter. The DeepTox algorithm, which is based on deep learning methods, gave outstanding results in the Tox21 data challenge in which the participating groups attempted to predict the toxic effects of 12 000 environmental chemicals and drugs. Once a deep neural network has “learned” to detect toxic features of molecules, it can be used to assess the toxicity of new compounds at an early stage in the drug discovery pipeline.

化合物的毒理学特征也是重要的参数。 基于深度学习方法的DeepTox算法在Tox21数据挑战中获得了出色的结果，在该挑战中，参与小组试图预测12000种环境化学品和药物的毒性作用。 一旦“学习”了一个深层的神经网络以检测分子的毒性特征，就可以在药物开发流程的早期阶段将其用于评估新化合物的毒性。

Planning a chemical synthesis pathway

规划化学合成途径

Once it has been decided that a molecule holds promise for development into a potential medicine, the search for an optimal chemical synthesis pathway begins. A technique called retrosynthetic analysis recursively searches for “backward” reaction pathways until a set of simpler, readily-available precursor molecules is obtained. AI performs this task much more efficiently than its human counterparts, typically taking a matter of seconds.

一旦确定一种分子有望发展成为一种潜在的药物，就开始寻找最佳的化学合成途径。 称为逆合成分析的技术递归地搜索“向后”的React途径，直到获得一组更简单，易于获得的前体分子。 人工智能执行任务的效率要比人类同行高得多，通常只需几秒钟 。

Digitising chemical synthesis

化学合成数字化

Finally, to make the molecule of interest, a medicinal chemist will spend most of her time in the laboratory manipulating flasks and funnels to react, purify and characterise compounds. Recently, the Chemputer platform has been developed to codify standard “recipes” for robotic synthesis. The system has been validated by synthesising several pharmaceutical compounds, without any human intervention, with comparable (or increased) product yields than those achieved manually.

最后，要制造感兴趣的分子，药用化学家将把大部分时间都花在实验室操作烧瓶和漏斗上，以进行React，纯化和表征化合物。 最近，已经开发了Chemputer平台，以编纂用于机器人合成的标准“配方”。 该系统已通过在无需任何人工干预的情况下，合成了几种药物化合物 ，与手动获得的产品产量相比(或提高了)的产品收率进行了验证。

Preliminary efforts to accelerate drug discovery by incorporating AI, such as the discovery of halicin above, are promising. Last year, Takeda Pharmaceuticals and “digital biology” company Recursion announced that they had identified potential drug candidates for more than 60 unique indications in just one-and-a-half years — much faster than the traditional drug discovery pipeline of approximately a decade.

通过并入AI来加快药物发现的初步努力是有希望的，例如上述盐霉素的发现。 去年，武田制药和“数字生物学”公司Recursion 宣布 ，他们在短短的一年半的时间内就已经找到了60多种独特适应症的潜在候选药物，这比大约十年来传统的药物开发流程要快得多。

The prospect of accelerated drug discovery is enticing, but what does it mean for traditional research scientists? Will they eventually be replaced by algorithms and digital platforms such as the Chemputer? Perhaps this is the time for laboratories and organisations to encourage the evolution of a new type of research scientist — a scientist for the 21st century, who is equally skilled at handling pipettes and harnessing technology to make useful predictions.

加速药物发现的前景诱人，但这对传统研究科学家意味着什么？ 它们最终会被算法和数字平台(如Chemputer)取代吗？ 也许现在是实验室和组织鼓励新型研究科学家的时代了-21世纪的科学家，他同样擅长处理移液器并利用技术做出有用的预测。