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Machine learning algorithms can help scientists predict chemical toxicity to a similar degree of accuracy as animal testing, according to a paper published in Toxicological Sciences.
A whopping €3bn (over $3.5bn) is spent every year to study how the negative impacts of chemicals on animals like rats, rabbits or monkeys. The top nine most frequently tested safety experiments resulted in the death of the poor critters 57 per cent of the time in Europe in 2011. By using software, chemists may be able to spend less on animal testing and save more creatures.
Team - Researchers - Range - Databases - Chemicals
First, a team of researchers scoured through a range of databases to label 80,908 different chemicals. Some of these labels include things like corrosion, irritation, serious eye damage, or being hazardous to the ozone layer.
Next, they used a mixture of unsupervised and supervised learning to build a statistical model that groups chemicals together based on how chemically and toxically similar they are to each other. The unsupervised method uses the...
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