Biochemists have had some success designing drugs to meet specific goals. But much of drug development remains a tedious grind, screening hundreds to thousands of chemicals for a "hit" that has the effect you're looking for. There have been several attempts to perform this grind in silico, using computers to analyze chemicals, but they had mixed results. Now, a US-Canadian team reports that it modified a neural network to deal with chemistry and used it to identify a potential new antibiotic. Two factors greatly influence the success of neural networks: the structure of the network itself and the training it undergoes.

It was the veritable search for a needle in a haystack. With drug-resistant bacteria on the rise, researchers at MIT were sifting through a database of more than 100 million molecules to identify a few that might have antibacterial properties. Fortunately, the search proved successful. But it wasn't a human who found the promising molecules. It was a machine learning program.

It was the veritable search for a needle in a haystack. With drug-resistant bacteria on the rise, researchers at MIT were sifting through a database of more than 100 million molecules to identify a few that might have antibacterial properties. Fortunately, the search proved successful. But it wasn't a human who found the promising molecules. It was a machine learning program.

Representations of molecules can mostly be summed up into two categories: (1) linear notations, which use chemical rules to create string representations of the molecule, and (2) graphs, which represent atoms as nodes and bonds as edges. Of the two, linear notations are more compact, require relatively little disk space to store, and tend to scale linearly as the molecule size grows. However, graph representations offer more versatility and can potentially capture more features. There are multiple types of linear notation generation, with the SMILES method being the most popular due to its simplicity. In brief, the notation is created by assigning a number to each atom in the molecule and traversing the molecule like a graph using depth-first-search.

As the world continues to grapple with the Covid-19 pandemic, another health crisis is looming: antibiotic resistance. Bacterial resistance is something that occurs naturally, but widespread antibiotic misuse has propelled antimicrobial resistance (AMR) to major global health threat status; at least 700,000 people are killed by drug-resistant superbugs every year – and by 2050, this number could reach 10 million. A report by the World Health Organization, published earlier this year, also found that none of the 43 antibiotics currently under development "sufficiently address the problem of drug resistance" in the bacteria considered most dangerous to public health. The situation, as it stands, looks bleak – but there is hope. Advances in technology are vastly improving the way researchers discover and develop drugs, and antibiotics are no exception.

At the beginning of the year, I have a feeling that Graph Neural Nets (GNNs) became a buzzword. As a researcher in this field, I feel a little bit proud (at least not ashamed) to say that I work on this. It was not always the case: three years ago when I was talking to my peers, who got busy working on GANs and Transformers, the general impression that they got on me was that I was working on exotic niche problems. Well, the field has matured substantially and here I propose to have a look at the top applications of GNNs that we have recently had. If this in-depth educational content on graph neural networks is useful for you, you can subscribe to our AI research mailing list to be alerted when we release new material.

Using a machine-learning algorithm, MIT researchers have identified a powerful new antibiotic compound. In laboratory tests, the drug killed many of the world's most problematic disease-causing bacteria, including some strains that are resistant to all known antibiotics. It also cleared infections in two different mouse models. 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. "We wanted to develop a platform that would allow us to harness the power of artificial intelligence to usher in a new age of antibiotic drug discovery," says James Collins, the Termeer Professor of Medical Engineering and Science in MIT's Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering.

In September 2020, Regina Barzilay was announced as the winner of the inaugural AAAI Squirrel AI award. Regina was formally presented with the prize during an award ceremony at the AAAI2021 conference, following which she delivered an invited talk. She spoke about two particular areas of medicine that she has been researching: drug discovery and cancer diagnosis. It is well-known that the development of drugs is slow and expensive. Currently, drug discovery is primarily experimentally driven, with properties of molecules investigated empirically.

Anewly designed artificial intelligence tool based on the structure of the brain has identified a molecule capable of wiping out a number of antibiotic-resistant strains of bacteria, according to a study published on February 20 in Cell. The molecule, halicin, which had previously been investigated as a potential treatment for diabetes, demonstrated activity against Mycobacterium tuberculosis, the causative agent of tuberculosis, and several other hard-to-treat microbes. The discovery comes at a time when novel antibiotics are becoming increasingly difficult to find, reports STAT, and when drug-resistant bacteria are a growing global threat. The Interagency Coordination Group (IACG) on Antimicrobial Resistance convened by United Nations a few years ago released a report in 2019 estimating that drug-resistant diseases could result in 10 million deaths per year by 2050. Despite the urgency in the search for new antibiotics, a lack of financial incentives has caused pharmaceutical companies to scale back their research, according to STAT. "I do think this platform will very directly reduce the cost involved in the discovery phase of antibiotic development," coauthor James Collins of MIT tells STAT.

MIT researchers have identified a powerful new antibiotic compound using a machine-learning algorithm. A deep-learning model identifies a powerful new drug that can kill many species of antibiotic-resistant bacteria. Using a machine-learning algorithm, MIT researchers have identified a powerful new antibiotic compound. In laboratory tests, the drug killed many of the world's most problematic disease-causing bacteria, including some strains that are resistant to all known antibiotics. It also cleared infections in two different mouse models.