AI For Drug Design

See in  particular how these methods are designed

AI for Drug Design   By Sandrine Ceurstemont

Some recent breakthroughs in drug discovery have come about thanks to the use of artificial intelligence.

Traditional drug development is slow and expensive. It often takes more than 10 years for a new medicine to come to market, and it can cost up to $2.6 million. In the past few years, however, there has been a growing interest in using machine learning to help with the process.

"The idea is that you can screen billions of molecules on a computer and identify some which look promising, and then you just manufacture and test the small subset," says Regina Barzilay, Delta Electronics Professor of the Massachusetts Institute of Technology (MIT) Department of Electrical Engineering and Computer Science, and a member of the university's Computer Science and Artificial Intelligence Lab.

The vast size of chemical space is one of the main challenges when it comes to finding new drugs. Medicinal chemists look for new small molecules, and there could be up to 1 novemdecillion (1 followed by 60 zeros) of them, according to the American Chemical Society, more than some estimates of the number of stars in the universe. Although researchers have zeroed in on millions of these compounds through traditional methods, the number that have been synthesized and tested as drugs is thought to represent less than 0.1% of the potential drugs that exist. "The machine learning community identified it as an important area where we can contribute," says Barzilay.

There have been some recent breakthroughs in drug discovery, thanks to artificial intelligence (AI). In recent work, Barzilay and her colleagues used a deep learning system to discover a new antibiotic, which is a first. The newly discovered medicine proved effective against a wide range of bacteria in tests on mice, including tuberculosis and bacteria strains that have demonstrated resistance to current antibiotics.

Barzilay and her team decided to focus on antibiotics since a lack of new antibiotics is creating a growing health crisis. Existing antibiotics are no longer effective against many infections, as bacteria have grown resistant. Just eight new antibiotics with limited effectiveness have been approved since July 2017, according to a recent report by the World Health Organization.

To tackle the problem, the researchers developed a deep learning convolutional neural network (CNN) that can predict the antibiotic properties of new compounds. It was first trained to recognize molecules that inhibit the growth of E. coli bacteria by feeding it a collection of about 2,500 molecules whose antibacterial capabilities were known. Then, the system was presented with a library, called the Drug Repurposing Hub, containing over 6,000 molecules identified as potentially interesting to fight various human diseases.  It was asked to predict which molecules are both active against E. coli and had different structures from existing antibiotics.

One result was the new antibiotic halicin (named for the intelligent computer HAL in the movie 2001: A Space Odyssey). The medication  was being investigated as a potential treatment for diabetes.  .... "