
生命科学公司有望从人工智能推动的药物发现浪潮中获益,Citrini Research在测试Anthropic PBC的Claude Science后表示。
Life sciences companies stand to gain from a surge in artificial intelligence fueled drug discovery, says Citrini Research after testing Anthropic PBC’s Claude Science.
Charles River Laboratories International Inc.和Revvity Inc.是该独立投资研究机构的首选生命科学股票之一,可能从人工智能在药物发现方面的当前进展中受益。Citrini的最新看法是在其使用Anthropic工具创建一种新药候选物之后得出的,这一过程揭示了人工智能可以加快研究时间并推动实验验证需求。
Charles River Laboratories International Inc. and Revvity Inc. are among the independent investment research firm’s top life sciences picks that could benefit from AI’s current progress in drug discovery. Citrini’s latest call comes after it used the Anthropic tool to create a new drug candidate, a process that revealed AI can speed up the time for research and spur demand for experimental validation.
“随着这些‘虚拟生物学家’被更广泛地采用,任何向它们发出第一张现实世界账单的公司,其需求都应该上升,”Citrini分析师周二在一份备忘录中写道。
“As these ‘virtual biologists’ become more widely adopted, demand should rise for any company that sends them their first real-world invoice,” Citrini analysts wrote in a note on Tuesday.
人工智能的使用正在成为开发针对众多疾病的强效新药竞赛中的常规环节,制药商越来越多地使用人工智能来识别和设计有前景的新分子,以及挽救那些本可能被放弃的药物。过去一年中,与人工智能相关的生物科技股票飙升,投资者押注计算机可以快速解决那些历史上让研究人员花费数年时间才能解决的科学问题。
The use of AI is becoming a routine part of the race to develop powerful new medicines across numerous diseases, with drugmakers increasingly using AI to identify and design promising new molecules as well as salvage medicines that might otherwise have been abandoned. Stocks of AI-linked biotechs have surged over the past year on bets that computers can quickly tackle scientific problems that have historically taken researchers years to solve.
“在发现被压缩、实验验证需求扩大的世界里,合同研究组织就如同生物学的云基础设施,”研究人员补充道,指的是制药公司聘请来管理和运行临床研究的公司。“细胞、检测、试剂和患者样本将变得像计算资源一样。”阅读:Anthropic类似CRISPR的发现引发科学家谨慎。例如,Charles River是用于药物测试的猴子最大供应商之一,这是医学研究的一个重要领域,近年来一直面临短缺。
“In a world where discovery is compressed and demand for experimental validation expands, the contract research organization becomes akin to cloud infrastructure for biology,” the researchers added, referring to firms that drug companies hire to manage and run clinical studies. “Cells, assays, reagents, and patient samples become like compute.”Read: Anthropic Crispr-Like Findings Draw Caution From Scientists Charles River, for instance, is one of the largest providers of monkeys used for drug testing, an important area of medical research that’s suffered a shortage in recent years.
“只要这种交易持续下去,只要人们关注瓶颈问题,生物技术领域几乎没有比猴子更受瓶颈制约的了。”据Citrini称,对于制造研究仪器和软件的Revvity而言,其涉足围绕AI药物设计的实验工作是一大利好。
“To the degree this trade keeps going, and the degree that people focus on bottlenecks, there’s just about nothing more bottlenecked in biotech than monkeys.”For Revvity, which makes instruments and software for research, its exposure to the experimental work surrounding AI drug design is a positive, according to Citrini.
Revvity生产许多研发所需的“镐和铲子”(基础工具),分析师因此称该公司为“生物技术领域的算力”。Citrini还点名实验室自动化公司Tecan Group AG,将其列为首选股票之一。
Revvity makes many of the “picks and shovels” for research and development, with the analysts dubbing the firm as “the compute of biotech.”Citrini also called out lab automation firm Tecan Group AG among its top stock picks.
Tecan“解决的是另一个不同的(且可能更大的)制约因素:如何在不以同等速度增加人工协调的情况下,将大量提议的实验转化为可重复的实验室工作。”鉴于融资环境艰难,且投资者多年来发现了更令人兴奋的投资去处,生命科学板块经历了疫情繁荣后的回落。虽然一个包含19只股票的生命科学指数已从新冠疫情后的低迷中稳步复苏,今年以来上涨逾18%,但该指数仍较疫情时期的峰值下跌超过10%。
Tecan “addresses the different (and potentially larger) constraint of how to convert a large number of proposed experiments into repeatable laboratory work without increasing manual coordination at the same rate.”The life sciences sector has seen a reversal of the pandemic boom, given a difficult funding environment, and years of investors finding more exciting places for their money. While a 19-member index of life sciences stocks has steadily recovered from the post Covid lull and is up over 18% so far this year, the gauge is still down more than 10% from its pandemic-era peak.
Citrini表示:“这是一个被忽视的板块,却拥有AI题材的机会,具备我们最青睐的那种交易设置要素——处于周期性低点的长期故事。”
“A neglected sector with the chance for an AI narrative, it had the makings of our favorite type of setup — a secular story at cyclical lows,” Citrini said.
在做出这一看涨判断前,一组Citrini分析师与Anthropic合作,测试人工智能在药物发现中的作用。在一份题为《Citrini在Anthropic办公室搭建实验环境,利用其本地网络和一个内部通用AI研究模型创造了一种新药物候选物。
Prior to the bullish call, a group of Citrini analysts partnered with Anthropic to test the role of artificial intelligence in drug discovery. In a paper titled Citrini set up in Anthropic’s offices, using its local network and an internal, general-purpose AI research model to create a new drug candidate.

在Anthropic生命科学团队的支持下,Citrini决定创造一种可作为蝎子抗毒血清的分子。Claude Science仅用几天就生成了约180种分子设计,符合研究人员提供的约35页提示词要求。
Citrini, with the support of Anthropic’s Life Sciences team, decided to create a molecule that could work as a scorpion antivenom. It took just days for Claude Science to produce about 180 molecule designs that fit the roughly 35-page prompt the researchers had provided.
Citrini的研究人员在报告中写道:“AI将大幅压缩将一个想法转化为实验所需的时间。这意味着民主化。”设计好的蛋白质被送往Twist Bioscience Corp.进行进一步合成,在针对五种毒素的测试中,211个设计中有17个达到了至少结合一种靶标毒素的严格标准。
“AI will drastically compress the time it takes to turn an idea into an experiment,” Citrini researchers wrote in the report. “That means democratization.”The designed proteins were sent to Twist Bioscience Corp. for further synthesis, with 17 out of 211 designs achieving the strict criterion of binding to at least one target toxin out of the five toxins they were tested against.
研究人员发现:“这是一个不错的结果。起初,我们的预期相当低。我们并不真正指望能设计出实际能结合的东西。”该实验尚未测试设计的蛋白质是否真能阻止毒素。
“This was a good result,” the researchers found. “Going in, our expectations were quite low. We didn’t really expect to design anything that actually bound.”The experiment hasn’t yet tested whether the designed proteins actually stop the toxin.
虽然研究人员认为Claude实验总体成功,能够将“一年份的工作压缩到几天内”,但他们也承认,AI在解决药物在人体内如何起作用的复杂性和不可预测性方面面临困难。
While researchers viewed the Claude experiment as generally successful in its ability to compress “a year worth of work into a couple days,” they acknowledged the difficulty for AI when it comes to solving the complexity and unpredictability of how drugs work in the human body.
尽管如此,Citrini认为,AI有助于针对熟悉的靶标产生更好的治疗方法,而让现有药物更具实用性的过程能释放巨大价值。
Still, Citrini argues that AI can help produce better treatments against familiar targets and that the process of making an existing drug more practical can unlock huge value.
研究人员写道:“我们不需要AI治愈癌症就能产生巨大影响。对市场而言,重要的是现在不是AI最终将把医学带向何方,而是它未来几年将改变什么。”
“We don’t need AI to cure cancer to have a massive impact,” the researchers wrote. “What matters for the market right now is not where AI will ultimately take medicine, but what it will change over the next few years.”