来源：《新科学家》
原文刊登日期：2021年4月16日
Facebook claims that its new artificial intelligence can predict the way drugs interact with each other inside cells quicker than existing methods, enabling speedier discovery of new drug combinations to treat illnesses like cancer, but some researchers say it may not translate into results that will be useful in humans.
脸书声称，与现有方法相比，其新的人工智能技术可以更快地预测细胞内药物相互作用的方式，从而更快地发现治疗癌症等疾病的新药物组合，但一些研究人员表示，它可能无法转化为对人类有用的结果。
The system, developed by Facebook AI Research and the Helmholtz Centre in Germany, is claimed to be the first easy-to-use AI model able to estimate how different drugs will work in the body. It could speed up our ability to uncover new treatments for diseases like cancer. “Drug research often takes half a decade to develop a compound,” says Fabian Theis at the Helmholtz Centre, one of the authors of the work.
该系统由脸书人工智能研究中心和德国亥姆霍兹中心开发，据称是首个易于使用的人工智能模型，能够估计不同药物在体内的作用。它可以加速我们发现癌症等疾病新疗法的能力。“药物研究通常需要五年的时间才能开发出一种化合物，”该研究的作者之一、亥姆霍兹中心的费边·泰斯说。
The model works by measuring how individual cells change in response to treatment from a particular set of drugs and recording those responses. Such an approach could theoretically help tackle cancer tumours, which vary from person to person and react differently to the same treatment, says Eytan Ruppin at the US National Cancer Institute.
该模型的工作原理是测量单个细胞对一组特定药物治疗的反应，并记录这些反应。美国国家癌症研究所的Ruppin说，从理论上讲，这种方法可以帮助治疗癌症肿瘤，肿瘤因人而异，对相同治疗的反应也不同。
The AI factors in variables including the type of drug, what it is used in combination with, the dosage level, the time it is taken and the type of cell it targets. It can then use that information to predict the effect of drug combinations it hasn’t yet seen.
人工智能会考虑各种变量，包括药物类型、与之联合使用的药物、剂量水平、所用时间以及靶向细胞类型。然后，它可以利用这些信息来预测它尚未见过的药物组合的效果。
The research team behind it says humans can’t make these kinds of predictions: if they were given a pool of 100 different drugs, and asked to choose five to be given in three different doses – not uncommon in cancer treatment – there could be 19 billion possible drug regimes.
研究团队表示，人类无法做出这样的预测：如果给他们提供100种不同的药物，并要求他们选择5种以三种不同剂量给药——这在癌症治疗中并不罕见——那么可能会有190亿种药物组合。
The team tested the AI’s predictions against known combinations of drugs and found it was able to accurately forecast cell responses with over 90 per cent accuracy, says Theis. Unsurprisingly, the more drugs put into the model that the AI has seen before, the better its results. The AI will be released as an open-source tool for the research community to use and develop.
泰斯说，该团队测试了人工智能对已知药物组合的预测，发现它能够准确预测细胞反应，准确率超过90%。不出所料，AI以前见过的模型中投入的药物越多，其结果就越好。该人工智能将作为开源工具发布，供研究界使用和开发。
Andrei Lupas of the Max Planck Institute for Developmental Biology, Germany, calls the results “very promising” but says more work is needed. “The usefulness of the method will now hinge on a rigorous testing under double-blind conditions,” he says.
德国马克斯·普朗克发育生物学研究所的安德烈·卢普斯称这一结果“非常有希望”，但还需要做更多的工作。他说：“这种方法的有效性现在取决于在双盲条件下的严格测试。”
Ruppin says he is concerned that the claimed results don’t match up to the hype. The AI doesn’t predict whether a cell will live or die, but rather it predicts the changes in the RNA that the cell expresses when treated with a drug. This can show how the interior of a cell responds, but not necessarily whether it will survive or be killed off by the treatment, he says.
Ruppin说，他担心声称的结果与炒作不符。人工智能不能预测细胞的生死，但它能预测药物治疗后细胞表达的RNA的变化。他说，这可以显示细胞内部如何反应，但不一定表明细胞是否会存活或被治疗杀死。
He calls it an “important” first step in helping treat cancer, but points out that all the results are from test tubes. “We have cured cancer one hundred times in salines and mouse models. They have shown nothing at all that is relevant to patients,” he says.
他称这是帮助治疗癌症的“重要”第一步，但他指出，所有的结果都来自试管中。“我们已经用生理盐水和小鼠模型治愈了癌症100次。它们没有显示出任何与癌症病人有意义的东西。”他说。