Stephen Petranek: Your kids might live on Mars. Here’s how they’ll survive

May 12, 2017

It sounds like science fiction, but journalist Stephen Petranek considers it fact: within 20 years, humans will live on Mars. In this provocative talk, Petranek makes the case that humans will become a spacefaring species and describes in fascinating detail how we’ll make Mars our next home. “Humans will survive no matter what happens on Earth,” Petranek says. “We will never be the last of our kind.”

New nuclear magnetic resonance technique offers ‘molecular window’ for live disease diagnosis

May 7, 2017

University of Toronto Scarborough researchers have developed a new “molecular window” technology based on nuclear magnetic resonance (NMR) that can look inside a living system to get a high-resolution profile of which specific molecules are present, and extract a full metabolic profile.

“Getting a sense of which molecules are in a tissue sample is important if you want to know if it’s cancerous, or if you want to know if certain environmental contaminants are harming cells inside the body,” says Professor Andre Simpson, who led research in developing the new technique.*


An NMR spectrometer generates a powerful magnetic field that causes atomic nuclei to absorb and re-emit energy in distinct patterns, revealing a unique molecular signature — in this example: the chemical ethanol. (credit: adapted from the Bruker BioSpin “How NMR Works” video at

Simpson says there’s great medical potential for this new technique, since it can be adapted to work on existing magnetic resonance imaging (MRI) systems found in hospitals. “It could have implications for disease diagnosis and a deeper understanding of how important biological processes work,” by targeting specific biomarker molecules that are unique to specific diseased tissue.

The new approach could detect these signatures without resorting to surgery and could determine, for example, whether a growth is cancerous or benign directly from the MRI alone.

The technique could also provide highly detailed information on how the brain works, revealing the actual chemicals involved in a particular response. “It could mark an important step in unraveling the biochemistry of the brain,” says Simpson.

Overcoming magnetic distortion

Until now, traditional NMR techniques haven’t been able to provide high-resolution profiles of living organisms because of magnetic distortions from the tissue itself.  Simpson and his team were able to overcome this problem by creating tiny communication channels based on “long-range dipole interactions” between molecules.

The next step for the research is to test it on human tissue samples, says Simpson. Since the technique detects all cellular metabolites (substances such as glucose) equally, there’s also potential for non-targeted discovery.

“Since you can see metabolites in a sample that you weren’t able to see before, you can now identify molecules that may indicate there’s a problem,” he explains. “You can then determine whether you need further testing or surgery. So the potential for this technique is truly exciting.”

The research results are published in the journal Angewandte Chemie.

* Simpson has been working on perfecting the technique for more than three years with colleagues at Bruker BioSpin, a scientific instruments company that specializes in developing NMR technology. The technique, called “in-phase intermolecular single quantum coherence” (IP-iSQC), is based on some unexpected scientific concepts that were discovered in 1995, which at the time were described as impossible and “crazed” by many researchers. The technique developed by Simpson and his team builds upon these early discoveries. The work was supported by Mark Krembil of the Krembil Foundation and the Natural Sciences Engineering Research Council of Canada (NSERC).

Abstract of In-Phase Ultra High-Resolution In Vivo NMR

Although current NMR techniques allow organisms to be studied in vivo, magnetic susceptibility distortions, which arise from inhomogeneous distributions of chemical moieties, prevent the acquisition of high-resolution NMR spectra. Intermolecular single quantum coherence (iSQC) is a technique that breaks the sample’s spatial isotropy to form long range dipolar couplings, which can be exploited to extract chemical shift information free of perturbations. While this approach holds vast potential, present practical limitations include radiation damping, relaxation losses, and non-phase sensitive data. Herein, these drawbacks are addressed, and a new technique termed in-phase iSQC (IP-iSQC) is introduced. When applied to a living system, high-resolution NMR spectra, nearly identical to a buffer extract, are obtained. The ability to look inside an organism and extract a high-resolution metabolic profile is profound and should find applications in fields in which metabolism or in vivo processes are of interest.

Artificial intelligence could build new drugs faster than any human team

May 7, 2017

Artificial intelligence algorithms are being taught to generate art, human voices, and even fiction stories all on their own—why not give them a shot at building new ways to treat disease?

Atomwise, a San Francisco-based startup and Y Combinator alum, has built a system it calls AtomNet (pdf), which attempts to generate potential drugs for diseases like Ebola and multiple sclerosis. The company has invited academic and non-profit researchers from around the country to detail which diseases they’re trying to generate treatments for, so AtomNet can take a shot. The academic labs will receive 72 different drugs that the neural network has found to have the highest probability of interacting with the disease, based on the molecular data it’s seen.

Atomwise’s system only generates potential drugs—the compounds created by the neural network aren’t guaranteed to be safe, and need to go through the same drug trials and safety checks as anything else on the market. The company believes that the speed at which it can generate trial-ready drugs based on previous safe molecular interactions is what sets it apart.

Atomwise touts two projects that show the potential of AtomNet, drugs for multiple sclerosis and Ebola. The MS drug has been licensed to an undisclosed UK pharmacology firm, according to Atomwise, and the Ebola drug is being prepared for submission to a peer-reviewed publication.

Alexander Levy, the company’s COO and cofounder, said that AtomNet learns the interactions between molecules much like artificial intelligence learns to recognize images. Image recognition finds reduces patterns in images’ pixels to simpler representations, teaching itself the bounds of an idea like a horse or a desk lamp through seeing hundreds or thousands of examples.

“It turns out that the same thing that works in images, also works in chemistry,” Levy says. “You can take an interaction between a drug and huge biological system and you can decompose that to smaller and smaller interactive groups. If you study enough historical examples of molecules … and we’ve studied tens of millions of those, you can then make predictions that are extremely accurate yet also extremely fast.”

Atomwise isn’t the only company working on this technique. Startup BenevolentAI, working with Johnson & Johnson subsidiary Janssen, is also developing new ways to find drugs. TwoXAR is working on an AI-driven glaucoma medication, and Berg is working on algorithmically-built cancer treatments.

One of Atomwise’s advantages, Levy says, is that the network works with 3D models. To generate the drugs, the model starts with a 3D model of a molecule—for example a protein that gives a cancer cell a growth advantage. The neural network then generates a series of synthetic compounds (simulated drugs), and predicts how likely it would be for the two molecules to interact. If a drug is likely to interact with the specific molecule, it can be synthesized and tested.

Levy likens the idea to the automated systems used to model airplane aerodynamics or computer chip design, where millions of scenarios are mapped out within software that accurately represents how the physical world works.

“Imagine if you knew what a biological mechanism looked like, atom by atom. Could you reason your way to a compound that did the thing that you wanted?” Levy says.

Artificial intelligence could build new drugs faster than any human team