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Software Beats Animal Tests at Predicting Toxicity of Chemicals (nature.com)
Machine-learning software trained on masses of chemical-safety data is so good at predicting some kinds of toxicity that it now rivals -- and sometimes outperforms -- expensive animal studies, researchers report. From a report: Computer models could replace some standard safety studies conducted on millions of animals each year, such as dropping compounds into rabbits' eyes to check if they are irritants, or feeding chemicals to rats to work out lethal doses, says Thomas Hartung, a toxicologist at Johns Hopkins University in Baltimore, Maryland. "The power of big data means we can produce a tool more predictive than many animal tests."
In a paper published in Toxicological Sciences on 11 July, Hartung's team reports that its algorithm can accurately predict toxicity for tens of thousands of chemicals -- a range much broader than other published models achieve -- across nine kinds of test, from inhalation damage to harm to aquatic ecosystems. The paper "draws attention to the new possibilities of big data," says Bennard van Ravenzwaay, a toxicologist at the chemicals firm BASF in Ludwigshafen, Germany. "I am 100% convinced this will be a pillar of toxicology in the future." Still, it could be many years before government regulators accept computer results in place of animal studies, he adds. And animal tests are harder to replace when it comes to assessing more complex harms, such as whether a chemical will cause cancer or interfere with fertility."
In a paper published in Toxicological Sciences on 11 July, Hartung's team reports that its algorithm can accurately predict toxicity for tens of thousands of chemicals -- a range much broader than other published models achieve -- across nine kinds of test, from inhalation damage to harm to aquatic ecosystems. The paper "draws attention to the new possibilities of big data," says Bennard van Ravenzwaay, a toxicologist at the chemicals firm BASF in Ludwigshafen, Germany. "I am 100% convinced this will be a pillar of toxicology in the future." Still, it could be many years before government regulators accept computer results in place of animal studies, he adds. And animal tests are harder to replace when it comes to assessing more complex harms, such as whether a chemical will cause cancer or interfere with fertility."
Further proof that machine learning and AI has real world use. This is replacing the suffering of millions of animals today. Truly useful.
If we take a toxin that kills us, but had passed Animal Testing, then it is just God playing trick on us. But if it is something that an algorithm didn't realize to check then it is the fault of man. And some poor grad student will get hit with a multi-billion dollar lawsuit for not realizing such a chemical is harmful.
This is actually with my Tongue in Cheek response. But also a reflection of our culture and its intolerance for mistakes, to a point where we are being held back on progressing, because there could be new mistakes made, even though overall it is a much better solution.
If something is so important that you feel the need to post it on the internet... It probably isn't that important.
Maybe I'm old-fashioned but it seems to me that confirming that a substance is not toxic and predicting how toxic it may be are two very different things.
I say, rather than torture the animals, let us get rid of these government regulations and let the people who want these stupid products test them out.
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Avoiding overfitting to your training data is easy.
Models generalize to at least some data it wasn't trained on - that's the whole point. If they don't, they get thrown out.
But if the new compound is really dissimilar, enough that it can't be said to look like the data in the test set, then all bets are off.
I don't know enough about chemistry to know if that's likely to happen often. Hopefully, chemists will know if the compound they have an idea for is widely different from existing ones. Humans aren't out of the loop here.
xkcd is not in the sudoers file. This incident will be reported.