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Artificial intelligence used to discover powerful new antibiotic

Scientists have used artificial intelligence to discover a new type of antibiotic which could kill some of the world's most dangerous drug-resistant bacteria.

The researchers claim the AI technology is able to work more quickly and efficiently than existing efforts, because it checks more than 100 million chemical compounds in a matter of days to pick out potential antibiotics that kill bacteria.

'We're facing a growing crisis around antibiotic resistance, and this situation is being generated by both an increasing number of pathogens becoming resistant to existing antibiotics, and an anaemic pipeline in the biotech and pharmaceutical industries for new antibiotics,' said MIT's Professor James Collins, who is also co-founder of an antibiotic drug discovery firm EnBiotix.

MIT Technology Review

The mission will make use of 11 different instruments, including a NASA-funded instrument called MEGAE that will measure the elemental composition of both bodies (perhaps revealing signs of ancient water).

The four-legged MMX will attempt to land on Phobos and scoop up at least 10 grams of material from the surface, using a geological core sampler that can dig at least two centimeters deep.

It may seem a long way to travel to come back with such a tiny piece of the Martian system, but it’s actually a hundred times more material than Hayabusa2 is bringing back from Ryugu.

Phobos has long been suggested as a potential stepping-stone to traveling to the Red Planet, and data from JAXA's mission might give us a better idea as to whether setting up shop on the Martian moon makes sense.

Artificial intelligence yields new antibiotic

The computer model, which can screen more than a hundred million chemical compounds in a matter of days, is designed to pick out potential antibiotics that kill bacteria using different mechanisms than those of existing drugs.

Current methods for screening new antibiotics are often prohibitively costly, require a significant time investment, and are usually limited to a narrow spectrum of chemical diversity.

'We're facing a growing crisis around antibiotic resistance, and this situation is being generated by both an increasing number of pathogens becoming resistant to existing antibiotics, and an anemic pipeline in the biotech and pharmaceutical industries for new antibiotics,' Collins says.

To try to find completely novel compounds, he teamed up with Barzilay, Professor Tommi Jaakkola, and their students Kevin Yang, Kyle Swanson, and Wengong Jin, who have previously developed machine-learning computer models that can be trained to analyze the molecular structures of compounds and correlate them with particular traits, such as the ability to kill bacteria.

The researchers tested it against dozens of bacterial strains isolated from patients and grown in lab dishes, and found that it was able to kill many that are resistant to treatment, including Clostridium difficile, Acinetobacter baumannii, and Mycobacterium tuberculosis.

'When you're dealing with a molecule that likely associates with membrane components, a cell can't necessarily acquire a single mutation or a couple of mutations to change the chemistry of the outer membrane.

In contrast, the bacteria started to develop resistance to the antibiotic ciprofloxacin within one to three days, and after 30 days, the bacteria were about 200 times more resistant to ciprofloxacin than they were at the beginning of the experiment.

Optimized molecules After identifying halicin, the researchers also used their model to screen more than 100 million molecules selected from the ZINC15 database, an online collection of about 1.5 billion chemical compounds.

For example, they could train the model to add features that would make a particular antibiotic target only certain bacteria, preventing it from killing beneficial bacteria in a patient's digestive tract.

Is artificial intelligence making racial profiling worse?

In 1992, police violence and the acquittal of four police officers who beat black motorist Rodney King culminated in riots that killed more than 50 people.

A 2019 report found that the LAPD pulled over black drivers four times as often as white drivers, and Latino drivers three times as often as whites, despite white drivers being more likely to have weapons, drugs or other contraband.

Traditionally, police have stepped in to enforce the law after a crime has occurred, but advancements in artificial intelligence have helped create what are called 'predictive policing' programs.

In 2011, the LAPD instituted a program they helped develop called PredPol, a location-based program that uses an algorithm to sift through historical crime data and predict where the next vehicle theft or burglary may occur.

On the surface, using objective data to predict crime risk seems like a promising way to prevent subjective judgments or implicit bias about where to deploy police.

'If you unthinkingly develop a data-driven policing system based on past police practices, you're kind of going to reify past police practices,' he said.

Because historic crime data is biased through the practice of racialized enforcement of law, predictive policing will inherently reinforce and perpetuate this structural racism.'

But during the public comment period at a police commission meeting reviewing the audit, a community member voiced his concerns that location-based predictive policing is a covert way to justify racial profiling.

In a question directed toward the police commissioners' panel, the man continued, 'You said yourself, 'PredPol is designed to protect property.' You all value property more than lives?'

'I do believe that the effort of location-based strategies, of identifying places that form concentrations of crime, locations which are suffering from instances of violence and serious crime, are methods we should be aware of,' the chief said.

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