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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."
RTFA: https://watermark.silverchair....
"Practical use of structure activity relationships has therefore been largely limited to so-called read-across, i.e. the pragmatic comparison to one or few similar chemicals...This subjective expert-driven approach cannot be quickly applied to large numbers of chemicals. Read-across dependence on human opinion makes evaluation of the technique difficult and prevents reliable estimates of method reproducibility."
Do you know what that means? It means using statistical analysis, on large amounts of data, to compare the chemical structures of different chemicals to determine the biological properties of each substance. This means that a new chemical can be compared to an existing chemical and if found similar we would know the biological properties and effects of that chemical. Thus animal testing on a new chemical that is comparable to an existing chemical, which has already been tested on animals, would not be necessary.
That's not AI. That's not machine learning. It's statistical analysis on a large scale.
It does not do away with animal testing. One needs to know the effects. Without animal testing you have no understanding of what the chemical will do. The only reason you would not need to perform animal testing is if testing has already been done and the new chemical has similar properties to previously tested chemicals.
Jackass.