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Parlaying genomics and technology into pharmaceutical success is something Cohen has done before.
利用基因组学和技术取得制药成功是科恩以前做过的事情。
He was a cofounder of Millennium Pharmaceuticals, a U.S. oncology-drug maker that helped develop the multiple-myeloma treatment Velcade.
他曾是美国肿瘤药物制造商千禧制药公司的联合创始人，该公司曾帮助研发了一款治疗多发性骨髓瘤的药物Velcade。
Cohen is bullish that Pharnext can be successful with A.I., but he is also aware of the technology's limitations.
科恩看好Pharnext可以与AI取得成功，但他也意识到了这项技术的局限性。
Google's -AlphaZero, an A.I. program, was able to beat the world's human masters at the Chinese strategy game Go, without using any prior human knowledge.
谷歌的AI程序AlphaZero，能够在不使用任何先验人类知识的情况下，在中国围棋策略游戏中击败世界上的人类大师。
But as Cohen points out, Go has a finite set of rules, which AlphaZero knew completely.
但正如科恩所指出的，围棋有一套有限的规则，AlphaZero完全清楚这些规则。
In biology, thanks in part to pleiotropy, the rules are not fully known—and may never be.
在生物学中，由于基因多效性的原因，其规则还未被完全了解——并且可能永远都不会被了解。
But thoughtfully designed A.I. has enabled Pharnext to build models around the rules that are known and make choices accordingly.
但经过精心设计的AI让Pharnext能够围绕已知的规则构建模型，并据此做出选择。
Out of the universe of 10,000 known drugs, the company's discovery model takes in an assortment of 2,000 that are both out of patent and "marketed"—
在一万种已知药物中，该公司的发现模型包含了2000种既已过了专利期又已“上市”的药物——
that is, already judged both therapeutically effective and safe enough to be sold to the public.
即被判定有疗效且足够安全，可以对公众进行销售。
To develop its CMT drug, Pharnext first spent about a year assembling its network model for the disease—
为了研发其CMT药物，Pharnext首先花了大约一年的时间来组装这种疾病的网络模型——
a framework comparable to GNS's Parkinson's map, showing how nervous and muscular problems "cascade" from the relevant gene mutation.
一种能够与GNS的帕金森地图相媲美的框架，呈现的是神经和肌肉问题是如何“级联”相关基因突变的。
Based on this mechanism, the computer model arrived at a short list of 57 candidate drugs that addressed various points in the cascade.
基于这种机制，计算机模型得到了57种候选药物的简短列表，这些候选药物针对着级联中的各个点。
Pharnext tested those drugs one by one in vitro, generating a shorter list of 22 to be tested in mice,
Pharnext对这些药物逐一进行了体外测试，生成了一份更短的清单，其中22种将在老鼠身上进行测试，
which finally yielded the three-drug combination that went to human clinical trials.
最终产生了三种药物的组合，并进行了人体临床试验。
The recent positive Phase III results confirmed that the PXT3003 cocktail is acting at various points in the cascade.
近期第三期阳性结果证实了，PXT3003药物组合正在作用于级联中的各个点上。
Without the A.I. model, many more years of preclinical testing would have been required beyond the three years it took Pharnext, says Cohen.
科恩表示，没有了AI模型，除了Pharnext所花费的三年时间，可能还需要更多时间来进行临床前测试。
"With 2,000 drugs to start with, I could produce all possible combinations, a billion possibilities" to test in vitro.
“根据一开始的两千种药物，我可以制造出所有可能的组合，一百万种可能”来进行体外测试。
That's a recipe for countless false positives and dead ends—years of frustration, for now forestalled.
这就是无数假阳性和死胡同的秘诀——多年的失望，现在该先发制人了。
Pharnext's shares, which trade on the Paris stock exchange, have more than doubled since October's Phase III results announcement.
自10月第三阶段结果宣布以来，在巴黎股票交易所交易的Pharnext股票已上涨逾一倍。
The company has spent about 120 million euros ($135 million) over the past decade on research and development—a very modest figure by pharma standards.
在过去的十年里，该公司在研发上花费了大约1.2亿欧元（即1.35亿美元）——以制药行业的标准来衡量，这是一个非常合理的数字。
It has never made a profit, but analysts estimate that if PXT3003 reaches the market, revenue—9 million euros in 2018—could soar starting in 2020.
该公司从未实现过盈利，但分析师估计，如果PXT3003上市，该公司的收入——2018年为900万欧元——将从2020年开始猛增。
(GNS Healthcare is privately held and does not disclose spending or revenue.)
（GNS Healthcare是一家私营企业，并且没有披露支出或收入。）
Beyond possible victories for investors, the advances at Pharnext and GNS point the way to A.I.'s growing up—and pharmacology along with it.
除了可能为投资者带来收益，Pharnext和GNS的进步也为AI的发展指明了道路——药理学也随之而生。
The ability to reason about causality, and to explore counterfactual questions, is a threshold that users of artificial intelligence have long sought to cross.
推理因果关系，以及探究反设事实问题的能力，是AI用户长期以来一直寻求跨越的一道门槛。
The computer models at these startups are making a foray in that direction as they manage and tame a bewildering number of variables.
随着它们管理并驯服了大量令人眼花缭乱的变量，这些初创公司的计算机模型正朝着这个方向进军。
Even the underlying definition of disease may evolve. As scientists are learning, these definitions have been overly simplistic.
疾病的基本定义也可能进化。随着科学家的不断了解，这些定义被过度简化了。
A study in the journal Bioinformatics last year noted that attempts to treat tumors
去年《Bioinformatics》期刊上的一项研究指出，
are hampered by the fact that genetic mutations in cancer are "fundamentally heterogeneous":
治疗肿瘤的尝试受到了这样一个事实的阻碍，即癌症的基因突变“从根本上是异质性的”：
What appears as one disease, or class of disease, in fact contains few commonalities and many differences from patient to patient.
表现为一种疾病或一类疾病的物质，实际上几乎没有共性，并且病人之间也有许多不同之处。
As it becomes clear that what's happening in any body differs sharply from the "on/off" model of one gene turning on a set of symptoms,
我们越来越清楚，在任何身体中发生的情况都与一个基因启动一系列症状的“开启/关闭”模式截然不同，
technology can help drug developers wrestle with the complexity.
技术能够帮助药物研发者全力解决这种复杂性。
Last, but hardly least, these A.I.-driven efforts offer a glimmer of economic hope.
最后但同样重要的是，这些由AI驱动的努力带来了一丝经济希望。
In an era in which the cost of drug development is a daunting obstacle,
在当今时代，药物研发成本是一个令人生畏的障碍，
smart algorithms may someday enable medical stakeholders to derive more value from the trillions of dollars that have already been spent on drug research.
聪明的算法将来可能会让医疗利益相关者从已经花费在药物研究上的数万亿美元中获得更多的价值。
In theory, with repurposing, "you don't need to design new drugs," Cohen avers. "My feeling is that with 50 drugs, we can treat everything."
理论上，有了再利用，“你就不需要设计新药物了，”科恩断言。“我觉得有了这50种药物，我们可以治愈一切。”
That would mean changing yet another definition: the meaning of "discovery."
这将意味着改变另一个定义：“发现”的含义。
译文由可可原创，仅供学习交流使用，未经许可请勿转载。
