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商业 - 科技

大药企如何利用人工智能技术改进药物

SY MUKHERJEE 2018年03月28日

人工智能可用来分析来自临床试验、病历、基因档案和临床前研究的海量数据,从中识别出模式,其效率远远超过单纯依靠研究人员。

制药公司的老板们认为,开发创新性的救命药物,需要有足够的投资回报。但最近,药物研发的投资回报率却低得可怜。据德勤(Deloitte)统计,2017年,12家规模最大的生物制药公司药物研发部门的投资回报率只有3.2%。2010年的投资回报率还达到了10.1%。

制药公司如何摆脱这种困境?一条途径可能是利用人工智能在早期(失败风险最高的阶段)提高新药发现的效率。德勤表示:“[人工智能]可用来分析来自临床试验、病历、基因档案和临床前研究的海量数据,从中识别出模式及趋势并提出假设,其效率远远超过单纯依靠研究人员。”

默克(Merck)、赛诺菲(Sanofi)和阿斯利康(AstraZeneca)等大型制药公司已经将人工智能技术引进到了实验室当中。2017年,阿斯利康与马萨诸塞州的初创公司BERG建立了合作伙伴关系,利用后者的人工智能平台寻找帕金森症等神经疾病的生物靶标和药物。

如何利用人工智能? BERG公司CEO尼文·R·纳拉因表示,首先要“回到生物学上来”。从健康者和患者身上提取组织样本,进行各种分子分析,结合临床数据,然后通过BERG的人工智能平台找出靶标。

纳拉因表示,在进行数据分析时,BERG会避开“公开的数据库。”他说道:“我们使用贝叶斯方法,而不是神经网络。并不是把一批数据放到模型里然后得出某种相关性这么简单。开始的时候并没有一个预先决定的假设,而是把所有数据都输入系统,让数据自己生成假设。”

简而言之,人工智能听起来像是一门不错的古董科学。很难想象!(财富中文网)

译者:刘进龙/汪皓 

Creating innovative, lifesaving medicines, say pharmaceutical company bosses, requires a sufficient return on investment. But lately, that ROI stinks. In 2017, according to Deloitte, the 12 largest bio- pharma companies got a mere 3.2% return out of their drug-research arms. In 2010, that number was 10.1%.

How can pharma break out of this rut? One avenue might be the use of artificial intelligence to improve drug discovery at the earliest stages (when the risk of failure is also the highest). “[A.I.] can help analyze large data sets from sources such as clinical trials, health records, genetic profiles, and preclinical studies; within this data, it can recognize patterns and trends and develop hypotheses at a much faster rate than researchers alone,” says Deloitte.

And Big Pharma names like Merck, Sanofi, and Astra- Zeneca, are already taking it to the lab. In 2017, AstraZeneca struck a partnership with BERG, a Massachusetts startup, to use the latter’s A.I. platform to home in on promising biological targets and possible agents against neurological diseases such as Parkinson’s.

So how does it work? For starters, says BERG CEO Niven R. Narain, by going “back to biology.” Tissue samples are taken from both healthy and sick

patients, analyzed on multiple molecular levels, combined with clinical data, and then fed through BERG’s A.I. platform to suss out targets.

For analyzing that data, BERG eschews “the publicly available databases,” says Narain. “We use a Bayesian approach rather than a neural network,” he says. “It’s not just taking a bunch of data, putting it through a model, and coming up with some correlation. You don’t start out with a predetermined hypothesis—you feed the system all this data and allow the data to generate the hypotheses.”

So, in short, A.I. sounds like good old-fashioned science. Go figure.

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