Using computers and machine learning to make sense of mountains of biomedical data is nothing new. But the team at the Massachusetts Institute of Technology, led by James Collins, who studies applications of systems biology to antibiotic resistance, and Regina Barzilay, an artificial-intelligence researcher, achieved success by developing a neural network that avoids scientists potentially limiting preconceptions about what to look for. Instead, the computer develops its own expertise.

Read: Antibiotic resistance is everyones problem

With this discovery platform, which has been made freely available, youre going to identify molecules that dont look like antibiotics youre used to seeing, Collins said. It really shows how you can use the emerging technology of deep learning in an innovative manner to discover new chemistries.

Ever since Alexander Fleming derived the first antibiotic from fungus, nature has been the font for our antibacterial drugs. But isolating, screening and synthesizing thousands of natural compounds for laboratory tests is extremely expensive and time-consuming.

To narrow the search, researchers have sought to understand how bacteria live and multiply, and then pursued compounds that attack those processes (such as by damaging bacterias cell walls, blocking their reproduction, or inhibiting their protein production). You start with the mechanisms, and then you reverse engineer the molecule, Barzilay said.

Even with the introduction of computer-assisted, high-throughput screening methods in the 1980s, however, progress in antibiotic development was virtually nonexistent in the decades that followed. Screening occasionally turned up drug candidates that were toxic to bacteria, but they were too similar to existing antibiotics to be effective against resistant bacteria. Pharmaceutical companies have since largely abandoned antibiotic development, despite the need, in favor of more lucrative drugs for chronic conditions.

Read: How antibiotic resistance could make common surgeries more dangerous

The new work by Barzilay, Collins, and their colleagues, however, takes a radically fresh, almost paradoxical approach to drug discovery: It ignores how the medicine works. Its an approach that can succeed only with the support of extremely powerful computing.

Behind the new antibiotic finding is a deep neural network, in which the nodes and connections of its learning architecture are inspired by the interconnected neurons in the brain. Neural networks, which are adept at recognizing patterns, are deployed across various industries and consumer technologies for uses such as image and speech recognition. Conventional computer programs might screen a library of molecules to find certain defined chemical structures, but neural networks can be trained to learn for themselves which structural signatures might be usefuland then find them.

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An Unexpected Ally in the War With Bacteria - The Atlantic