AI Algorithms Used To Develop Drugs That Fight Drug-Resistant Bacteria

One of the biggest challenges facing the medical industry is drug-resistant bacteria. Currently, there are some estimated 700,000 deaths due to drug-resistant bacteria, and more strains of drug-resistant bacteria are developing. Scientists and engineers are attempting to develop new methods of combatting drug-resistant bacteria. One method of developing new antibiotics is employing artificial intelligence and machine learning to isolate new compounds that could deal with new strains of super-bacteria.

As SingularityHub reported, a new antibiotic was designed with the assistance of AI. The antibiotic has been named halicin, after the AI HAL from 2001: A Space Odyssey. The newly developed antibiotic proved successful at eliminating some of the virile super-bacteria strains. The new antibiotic was discovered through the use of machine learning algorithms. Specifically, the machine learning model was trained using a large dataset comprised of approximately 2,500 compounds. Nearly half of the drugs used to train the model were drugs already approved by the FDA, while the other half of the training set was comprised of naturally occurring compounds. The team of researchers tweaked the algorithms to prioritize molecules that simultaneously possessed antibiotic properties but different from existing antibiotic structures. They then examined the results to determine which compounds would be safe for human consumption.

According to The Guardian, the drug proved extremely effective at fighting drug-resistant bacteria in a recent study. It is so effective because it degrades the membrane of the bacteria, which disables the ability of the bacteria to produce energy. For bacteria to develop defenses against the effects of halicin it could take more than a few genetic mutations, which gives halicin staying power. The research team also tested how the compound performed in mice, where it was able to successfully clear mice infected with a strain of bacteria resistant to all current antibiotics. With the results of the studies so promising, the research team is hoping to move into a partnership with a pharmaceutical entity and prove the drug safe for use by people.

James Collins, professor of bioengineering and senior author at MIT, and Regina Barzilay, computer science professor at MIT were both senior authors on the paper. Collins, Barzilay, and other researchers hope that algorithms like the type they used to design halicin could help fast-track the discovery of new antibiotics to deal with the proliferation of drug-resistant strains of the disease.

Halicin is far from the only drug compound discovered with the use of AI. The research team lead by Collin and Barzilay want to go farther and create new compounds training more models using around 100 million molecules pulled from the ZINC 15 database, an online library of over 1.5 billion drug compounds. Reportedly the team has already managed to find at least 23 different candidates that satisfy the criteria of being possibly safe for human use and structurally different from current antibiotics.

An unfortunate side effect of antibiotics is that, while they kill harmful bacteria, they also kill off the necessary gut bacteria that the human body needs. The research hopes that they could use techniques similar to the those used to create halicin to create antibiotics with fewer side effects, drugs less likely to harm the human gut microbiome.

Many other companies are also attempting to use machine learning to simplify the complex, long, and often expensive drug creation process. Other companies have also been training AI algorithms to synthesize new drug compounds. Just recently one company was able to develop a proof-of-concept drug in only a month and a half, a much shorter amount of time than the months or even years it can take to create a drug the traditional way.

Barzilay is optimistic that AI-driven drug discovery methods can transform the landscape of drug discovery in meaningful ways. Barzilay explained that the work on halicin is a practical example of how effective machine learning techniques can be:

“There is still a question of whether machine-learning tools are really doing something intelligent in healthcare, and how we can develop them to be workhorses in the pharmaceuticals industry. This shows how far you can adapt this tool.”