Tag Archives: virus

#439100 Video Friday: Robotic Eyeball Camera

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!):

RoboSoft 2021 – April 12-16, 2021 – [Online Conference]
ICRA 2021 – May 30-5, 2021 – Xi'an, China
RoboCup 2021 – June 22-28, 2021 – [Online Event]
DARPA SubT Finals – September 21-23, 2021 – Louisville, KY, USA
WeRobot 2021 – September 23-25, 2021 – Coral Gables, FL, USA
Let us know if you have suggestions for next week, and enjoy today's videos.

What if seeing devices looked like us? Eyecam is a prototype exploring the potential future design of sensing devices. Eyecam is a webcam shaped like a human eye that can see, blink, look around and observe us.

And it's open source, so you can build your own!

[ Eyecam ]

Looks like Festo will be turning some of its bionic robots into educational kits, which is a pretty cool idea.

[ Bionics4Education ]

Underwater soft robots are challenging to model and control because of their high degrees of freedom and their intricate coupling with water. In this paper, we present a method that leverages the recent development in differentiable simulation coupled with a differentiable, analytical hydrodynamic model to assist with the modeling and control of an underwater soft robot. We apply this method to Starfish, a customized soft robot design that is easy to fabricate and intuitive to manipulate.


Rainbow Robotics, the company who made HUBO, has a new collaborative robot arm.

[ Rainbow Robotics ]

Thanks Fan!

We develop an integrated robotic platform for advanced collaborative robots and demonstrates an application of multiple robots collaboratively transporting an object to different positions in a factory environment. The proposed platform integrates a drone, a mobile manipulator robot, and a dual-arm robot to work autonomously, while also collaborating with a human worker. The platform also demonstrates the potential of a novel manufacturing process, which incorporates adaptive and collaborative intelligence to improve the efficiency of mass customization for the factory of the future.

[ Paper ]

Thanks Poramate!

In Sevastopol State University the team of the Laboratory of Underwater Robotics and Control Systems and Research and Production Association “Android Technika” performed tests of an underwater anropomorphic manipulator robot.

[ Sevastopol State ]

Thanks Fan!

Taiwanese company TCI Gene created a COVID test system based on their fully automated and enclosed gene testing machine QVS-96S. The system includes two ABB robots and carries out 1800 tests per day, operating 24/7. Every hour 96 virus samples tests are made with an accuracy of 99.99%.

[ ABB ]

A short video showing how a Halodi Robotics can be used in a commercial guarding application.

[ Halodi ]

During the past five years, under the NASA Early Space Innovations program, we have been developing new design optimization methods for underactuated robot hands, aiming to achieve versatile manipulation in highly constrained environments. We have prototyped hands for NASA’s Astrobee robot, an in-orbit assistive free flyer for the International Space Station.

[ ROAM Lab ]

The new, improved OTTO 1500 is a workhorse AMR designed to move heavy payloads through demanding environments faster than any other AMR on the market, with zero compromise to safety.

[ ROAM Lab ]

Very, very high performance sensing and actuation to pull this off.

[ Ishikawa Group ]

We introduce a conversational social robot designed for long-term in-home use to help with loneliness. We present a novel robot behavior design to have simple self-reflection conversations with people to improve wellness, while still being feasible, deployable, and safe.

[ HCI Lab ]

We are one of the 5 winners of the Start-up Challenge. This video illustrates what we achieved during the Swisscom 5G exploration week. Our proof-of-concept tele-excavation system is composed of a Menzi Muck M545 walking excavator automated & customized by Robotic Systems Lab and IBEX motion platform as the operator station. The operator and remote machine are connected for the first time via a 5G network infrastructure which was brought to our test field by Swisscom.

[ RSL ]

This video shows LOLA balancing on different terrain when being pushed in different directions. The robot is technically blind, not using any camera-based or prior information on the terrain (hard ground is assumed).

[ TUM ]

Autonomous driving when you cannot see the road at all because it's buried in snow is some serious autonomous driving.

[ Norlab ]

A hierarchical and robust framework for learning bipedal locomotion is presented and successfully implemented on the 3D biped robot Digit. The feasibility of the method is demonstrated by successfully transferring the learned policy in simulation to the Digit robot hardware, realizing sustained walking gaits under external force disturbances and challenging terrains not included during the training process.

[ OSU ]

This is a video summary of the Center for Robot-Assisted Search and Rescue's deployments under the direction of emergency response agencies to more than 30 disasters in five countries from 2001 (9/11 World Trade Center) to 2018 (Hurricane Michael). It includes the first use of ground robots for a disaster (WTC, 2001), the first use of small unmanned aerial systems (Hurricane Katrina 2005), and the first use of water surface vehicles (Hurricane Wilma, 2005).


In March, a team from the Oxford Robotics Institute collected a week of epic off-road driving data, as part of the Sense-Assess-eXplain (SAX) project.

[ Oxford Robotics ]

As a part of the AAAI 2021 Spring Symposium Series, HEBI Robotics was invited to present an Industry Talk on the symposium's topic: Machine Learning for Mobile Robot Navigation in the Wild. Included in this presentation was a short case study on one of our upcoming mobile robots that is being designed to successfully navigate unstructured environments where today's robots struggle.

[ HEBI Robotics ]

Thanks Hardik!

This Lockheed Martin Robotics Seminar is from Chad Jenkins at the University of Michigan, on “Semantic Robot Programming… and Maybe Making the World a Better Place.”

I will present our efforts towards accessible and general methods of robot programming from the demonstrations of human users. Our recent work has focused on Semantic Robot Programming (SRP), a declarative paradigm for robot programming by demonstration that builds on semantic mapping. In contrast to procedural methods for motion imitation in configuration space, SRP is suited to generalize user demonstrations of goal scenes in workspace, such as for manipulation in cluttered environments. SRP extends our efforts to crowdsource robot learning from demonstration at scale through messaging protocols suited to web/cloud robotics. With such scaling of robotics in mind, prospects for cultivating both equal opportunity and technological excellence will be discussed in the context of broadening and strengthening Title IX and Title VI.

[ UMD ] Continue reading

Posted in Human Robots

#438731 Video Friday: Perseverance Lands on Mars

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!):

HRI 2021 – March 8-11, 2021 – [Online Conference]
RoboSoft 2021 – April 12-16, 2021 – [Online Conference]
ICRA 2021 – May 30-5, 2021 – Xi'an, China
Let us know if you have suggestions for next week, and enjoy today's videos.

Hmm, did anything interesting happen in robotics yesterday week?

Obviously, we're going to have tons more on the Mars Rover and Mars Helicopter over the next days, weeks, months, years, and (if JPL's track record has anything to say about it) decades. Meantime, here's what's going to happen over the next day or two:

[ Mars 2020 ]

PLEN hopes you had a happy Valentine's Day!

[ PLEN ]

Unitree dressed up a whole bunch of Laikago quadrupeds to take part in the 2021 Spring Festival Gala in China.

[ Unitree ]

Thanks Xingxing!

Marine iguanas compete for the best nesting sites on the Galapagos Islands. Meanwhile RoboSpy Iguana gets involved in a snot sneezing competition after the marine iguanas return from the sea.

[ Spy in the Wild ]

Tails, it turns out, are useful for almost everything.

[ DART Lab ]

Partnered with MD-TEC, this video demonstrates use of teleoperated robotic arms and virtual reality interface to perform closed suction for self-ventilating tracheostomy patients during COVID -19 outbreak. Use of closed suction is recommended to minimise aerosol generated during this procedure. This robotic method avoids staff exposure to virus to further protect NHS.

[ Extend Robotics ]

Fotokite is a safe, practical way to do local surveillance with a drone.

I just wish they still had a consumer version 🙁

[ Fotokite ]

How to confuse fish.

[ Harvard ]

Army researchers recently expanded their research area for robotics to a site just north of Baltimore. Earlier this year, Army researchers performed the first fully-autonomous tests onsite using an unmanned ground vehicle test bed platform, which serves as the standard baseline configuration for multiple programmatic efforts within the laboratory. As a means to transition from simulation-based testing, the primary purpose of this test event was to capture relevant data in a live, operationally-relevant environment.

[ Army ]

Flexiv's new RIZON 10 robot hopes you had a happy Valentine's Day!

[ Flexiv ]

Thanks Yunfan!

An inchworm-inspired crawling robot (iCrawl) is a 5 DOF robot with two legs; each with an electromagnetic foot to crawl on the metal pipe surfaces. The robot uses a passive foot-cap underneath an electromagnetic foot, enabling it to be a versatile pipe-crawler. The robot has the ability to crawl on the metal pipes of various curvatures in horizontal and vertical directions. The robot can be used as a new robotic solution to assist close inspection outside the pipelines, thus minimizing downtime in the oil and gas industry.

[ Paper ]

Thanks Poramate!

A short film about Robot Wars from Blender Magazine in 1995.

[ YouTube ]

While modern cameras provide machines with a very well-developed sense of vision, robots still lack such a comprehensive solution for their sense of touch. The talk will present examples of why the sense of touch can prove crucial for a wide range of robotic applications, and a tech demo will introduce a novel sensing technology targeting the next generation of soft robotic skins. The prototype of the tactile sensor developed at ETH Zurich exploits the advances in camera technology to reconstruct the forces applied to a soft membrane. This technology has the potential to revolutionize robotic manipulation, human-robot interaction, and prosthetics.

[ ETHZ ]

Thanks Markus!

Quadrupedal robotics has reached a level of performance and maturity that enables some of the most advanced real-world applications with autonomous mobile robots. Driven by excellent research in academia and industry all around the world, a growing number of platforms with different skills target different applications and markets. We have invited a selection of experts with long-standing experience in this vibrant research area

[ IFRR ]

Thanks Fan!

Since January 2020, more than 300 different robots in over 40 countries have been used to cope with some aspect of the impact of the coronavirus pandemic on society. The majority of these robots have been used to support clinical care and public safety, allowing responders to work safely and to handle the surge in infections. This panel will discuss how robots have been successfully used and what is needed, both in terms of fundamental research and policy, for robotics to be prepared for the future emergencies.

[ IFRR ]

At Skydio, we ship autonomous robots that are flown at scale in complex, unknown environments every day. We’ve invested six years of R&D into handling extreme visual scenarios not typically considered by academia nor encountered by cars, ground robots, or AR applications. Drones are commonly in scenes with few or no semantic priors on the environment and must deftly navigate thin objects, extreme lighting, camera artifacts, motion blur, textureless surfaces, vibrations, dirt, smudges, and fog. These challenges are daunting for classical vision, because photometric signals are simply inconsistent. And yet, there is no ground truth for direct supervision of deep networks. We’ll take a detailed look at these issues and how we’ve tackled them to push the state of the art in visual inertial navigation, obstacle avoidance, rapid trajectory planning. We will also cover the new capabilities on top of our core navigation engine to autonomously map complex scenes and capture all surfaces, by performing real-time 3D reconstruction across multiple flights.

[ UPenn ] Continue reading

Posted in Human Robots

#437872 AlphaFold Proves That AI Can Crack ...

Any successful implementation of artificial intelligence hinges on asking the right questions in the right way. That’s what the British AI company DeepMind (a subsidiary of Alphabet) accomplished when it used its neural network to tackle one of biology’s grand challenges, the protein-folding problem. Its neural net, known as AlphaFold, was able to predict the 3D structures of proteins based on their amino acid sequences with unprecedented accuracy.

AlphaFold’s predictions at the 14th Critical Assessment of protein Structure Prediction (CASP14) were accurate to within an atom’s width for most of the proteins. The competition consisted of blindly predicting the structure of proteins that have only recently been experimentally determined—with some still awaiting determination.

Called the building blocks of life, proteins consist of 20 different amino acids in various combinations and sequences. A protein's biological function is tied to its 3D structure. Therefore, knowledge of the final folded shape is essential to understanding how a specific protein works—such as how they interact with other biomolecules, how they may be controlled or modified, and so on. “Being able to predict structure from sequence is the first real step towards protein design,” says Janet M. Thornton, director emeritus of the European Bioinformatics Institute. It also has enormous benefits in understanding disease-causing pathogens. For instance, at the moment only about 18 of the 26 proteins in the SARS-CoV-2 virus are known.

Predicting a protein’s 3D structure is a computational nightmare. In 1969 Cyrus Levinthal estimated that there are 10300 possible conformational combinations for a single protein, which would take longer than the age of the known universe to evaluate by brute force calculation. AlphaFold can do it in a few days.

As scientific breakthroughs go, AlphaFold’s discovery is right up there with the likes of James Watson and Francis Crick’s DNA double-helix model, or, more recently, Jennifer Doudna and Emmanuelle Charpentier’s CRISPR-Cas9 genome editing technique.

How did a team that just a few years ago was teaching an AI to master a 3,000-year-old game end up training one to answer a question plaguing biologists for five decades? That, says Briana Brownell, data scientist and founder of the AI company PureStrategy, is the beauty of artificial intelligence: The same kind of algorithm can be used for very different things.

“Whenever you have a problem that you want to solve with AI,” she says, “you need to figure out how to get the right data into the model—and then the right sort of output that you can translate back into the real world.”

DeepMind’s success, she says, wasn’t so much a function of picking the right neural nets but rather “how they set up the problem in a sophisticated enough way that the neural network-based modeling [could] actually answer the question.”

AlphaFold showed promise in 2018, when DeepMind introduced a previous iteration of their AI at CASP13, achieving the highest accuracy among all participants. The team had trained its to model target shapes from scratch, without using previously solved proteins as templates.

For 2020 they deployed new deep learning architectures into the AI, using an attention-based model that was trained end-to-end. Attention in a deep learning network refers to a component that manages and quantifies the interdependence between the input and output elements, as well as between the input elements themselves.

The system was trained on public datasets of the approximately 170,000 known experimental protein structures in addition to databases with protein sequences of unknown structures.

“If you look at the difference between their entry two years ago and this one, the structure of the AI system was different,” says Brownell. “This time, they’ve figured out how to translate the real world into data … [and] created an output that could be translated back into the real world.”

Like any AI system, AlphaFold may need to contend with biases in the training data. For instance, Brownell says, AlphaFold is using available information about protein structure that has been measured in other ways. However, there are also many proteins with as yet unknown 3D structures. Therefore, she says, a bias could conceivably creep in toward those kinds of proteins that we have more structural data for.

Thornton says it’s difficult to predict how long it will take for AlphaFold’s breakthrough to translate into real-world applications.

“We only have experimental structures for about 10 per cent of the 20,000 proteins [in] the human body,” she says. “A powerful AI model could unveil the structures of the other 90 per cent.”

Apart from increasing our understanding of human biology and health, she adds, “it is the first real step toward… building proteins that fulfill a specific function. From protein therapeutics to biofuels or enzymes that eat plastic, the possibilities are endless.” Continue reading

Posted in Human Robots

#437749 Video Friday: NASA Launches Its Most ...

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here’s what we have so far (send us your events!):

AWS Cloud Robotics Summit – August 18-19, 2020 – [Virtual Conference]
CLAWAR 2020 – August 24-26, 2020 – [Virtual Conference]
ICUAS 2020 – September 1-4, 2020 – Athens, Greece
ICRES 2020 – September 28-29, 2020 – Taipei, Taiwan
AUVSI EXPONENTIAL 2020 – October 5-8, 2020 – [Virtual Conference]
IROS 2020 – October 25-29, 2020 – Las Vegas, Nevada
ICSR 2020 – November 14-16, 2020 – Golden, Colorado
Let us know if you have suggestions for next week, and enjoy today’s videos.

Yesterday was a big day for what was quite possibly the most expensive robot on Earth up until it wasn’t on Earth anymore.

Perseverance and the Ingenuity helicopter are expected to arrive on Mars early next year.

[ JPL ]

ICYMI, our most popular post this week featured Northeastern University roboticist John Peter Whitney literally putting his neck on the line for science! He was testing a remotely operated straight razor shaving robotic system powered by fluidic actuators. The cutting-edge (sorry!) device transmits forces from a primary stage, operated by a barber, to a secondary stage, with the razor attached.

[ John Peter Whitney ]

Together with Boston Dynamics, Ford is introducing a pilot program into our Van Dyke Transmission Plant. Say hello to Fluffy the Robot Dog, who creates fast and accurate 3D scans that helps Ford engineers when we’re retooling our plants.

Not shown in the video: “At times, Fluffy sits on its robotic haunches and rides on the back of a small, round Autonomous Mobile Robot, known informally as Scouter. Scouter glides smoothly up and down the aisles of the plant, allowing Fluffy to conserve battery power until it’s time to get to work. Scouter can autonomously navigate facilities while scanning and capturing 3-D point clouds to generate a CAD of the facility. If an area is too tight for Scouter, Fluffy comes to the rescue.”

[ Ford ]

There is a thing that happens at 0:28 in this video that I have questions about.

[ Ghost Robotics ]

Pepper is far more polite about touching than most humans.

[ Paper ]

We don’t usually post pure simulation videos unless they give us something to get really, really excited about. So here’s a pure simulation video.

[ Hybrid Robotics ]

University of Michigan researchers are developing new origami inspired methods for designing, fabricating and actuating micro-robots using heat.These improvements will expand the mechanical capabilities of the tiny bots, allowing them to fold into more complex shapes.

[ DRSL ]

HMI is making beastly electric arms work underwater, even if they’re not stapled to a robotic submarine.

[ HMI ]

Here’s some interesting work in progress from MIT’s Biomimetics Robotics Lab. The limb is acting as a “virtual magnet” using a bimodal force and direction sensor.

Thanks Peter!

[ MIT Biomimetics Lab ]

This is adorable but as a former rabbit custodian I can assure you that approximately 3 seconds after this video ended, all of the wires on that robot were chewed to bits.

[ Lingkang Zhang ]

During the ARCHE 2020 integration week, TNO and the ETH Robot System Lab (RSL) collaborated to integrate their research and development process using the Articulated Locomotion and MAnipulation (ALMA) robot. Next to the integration of software, we tested software to confirm proper implementation and development. We also captured visual and auditory data for future software development. This all resulted in the creation of multiple demo’s to show the capabilities of the teleoperation framework using the ALMA robot.

[ RSL ]

When we talk about practical applications quadrupedal robots with foot wheels, we don’t usually think about them on this scale, although we should.

[ RSL ]

Juan wrote in to share a DIY quadruped that he’s been working on, named CHAMP.

Juan says that the demo robot can be built in less than US $1000 with easily accessible parts. “I hope that my project can provide a more accessible platform for students, researchers, and enthusiasts who are interested to learn more about quadrupedal robot development and its underlying technology.”


Thanks Juan!

Here’s a New Zealand TV report about a study on robot abuse from Christoph Bartneck at the University of Canterbury.

[ Paper ]

Our Robotics Studio is a hands on class exposing students to practical aspects of the design, fabrication, and programming of physical robotic systems. So what happens when the class goes virtual due to the covid-19 virus? Things get physical — all @ home.

[ Columbia ]

A few videos from the Supernumerary Robotic Devices Workshop, held online earlier this month.

“Handheld Robots: Bridging the Gap between Fully External and Wearable Robots,” presented by Walterio Mayol-Cuevas, University of Bristol.

“Playing the Piano with 11 Fingers: The Neurobehavioural Constraints of Human Robot Augmentation,” presented by Aldo Faisal, Imperial College London.

[ Workshop ] Continue reading

Posted in Human Robots

#437673 Can AI and Automation Deliver a COVID-19 ...

Illustration: Marysia Machulska

Within moments of meeting each other at a conference last year, Nathan Collins and Yann Gaston-Mathé began devising a plan to work together. Gaston-Mathé runs a startup that applies automated software to the design of new drug candidates. Collins leads a team that uses an automated chemistry platform to synthesize new drug candidates.

“There was an obvious synergy between their technology and ours,” recalls Gaston-Mathé, CEO and cofounder of Paris-based Iktos.

In late 2019, the pair launched a project to create a brand-new antiviral drug that would block a specific protein exploited by influenza viruses. Then the COVID-19 pandemic erupted across the world stage, and Gaston-Mathé and Collins learned that the viral culprit, SARS-CoV-2, relied on a protein that was 97 percent similar to their influenza protein. The partners pivoted.

Their companies are just two of hundreds of biotech firms eager to overhaul the drug-discovery process, often with the aid of artificial intelligence (AI) tools. The first set of antiviral drugs to treat COVID-19 will likely come from sifting through existing drugs. Remdesivir, for example, was originally developed to treat Ebola, and it has been shown to speed the recovery of hospitalized COVID-19 patients. But a drug made for one condition often has side effects and limited potency when applied to another. If researchers can produce an ­antiviral that specifically targets SARS-CoV-2, the drug would likely be safer and more effective than a repurposed drug.

There’s one big problem: Traditional drug discovery is far too slow to react to a pandemic. Designing a drug from scratch typically takes three to five years—and that’s before human clinical trials. “Our goal, with the combination of AI and automation, is to reduce that down to six months or less,” says Collins, who is chief strategy officer at SRI Biosciences, a division of the Silicon Valley research nonprofit SRI International. “We want to get this to be very, very fast.”

That sentiment is shared by small biotech firms and big pharmaceutical companies alike, many of which are now ramping up automated technologies backed by supercomputing power to predict, design, and test new antivirals—for this pandemic as well as the next—with unprecedented speed and scope.

“The entire industry is embracing these tools,” says Kara Carter, president of the International Society for Antiviral Research and executive vice president of infectious disease at Evotec, a drug-discovery company in Hamburg. “Not only do we need [new antivirals] to treat the SARS-CoV-2 infection in the population, which is probably here to stay, but we’ll also need them to treat future agents that arrive.”

There are currentlyabout 200 known viruses that infect humans. Although viruses represent less than 14 percent of all known human pathogens, they make up two-thirds of all new human pathogens discovered since 1980.

Antiviral drugs are fundamentally different from vaccines, which teach a person’s immune system to mount a defense against a viral invader, and antibody treatments, which enhance the body’s immune response. By contrast, anti­virals are chemical compounds that directly block a virus after a person has become infected. They do this by binding to specific proteins and preventing them from functioning, so that the virus cannot copy itself or enter or exit a cell.

The SARS-CoV-2 virus has an estimated 25 to 29 proteins, but not all of them are suitable drug targets. Researchers are investigating, among other targets, the virus’s exterior spike protein, which binds to a receptor on a human cell; two scissorlike enzymes, called proteases, that cut up long strings of viral proteins into functional pieces inside the cell; and a polymerase complex that makes the cell churn out copies of the virus’s genetic material, in the form of single-stranded RNA.

But it’s not enough for a drug candidate to simply attach to a target protein. Chemists also consider how tightly the compound binds to its target, whether it binds to other things as well, how quickly it metabolizes in the body, and so on. A drug candidate may have 10 to 20 such objectives. “Very often those objectives can appear to be anticorrelated or contradictory with each other,” says Gaston-Mathé.

Compared with antibiotics, antiviral drug discovery has proceeded at a snail’s pace. Scientists advanced from isolating the first antibacterial molecules in 1910 to developing an arsenal of powerful antibiotics by 1944. By contrast, it took until 1951 for researchers to be able to routinely grow large amounts of virus particles in cells in a dish, a breakthrough that earned the inventors a Nobel Prize in Medicine in 1954.

And the lag between the discovery of a virus and the creation of a treatment can be heartbreaking. According to the World Health Organization, 71 million people worldwide have chronic hepatitis C, a major cause of liver cancer. The virus that causes the infection was discovered in 1989, but effective antiviral drugs didn’t hit the market until 2014.

While many antibiotics work on a range of microbes, most antivirals are highly specific to a single virus—what those in the business call “one bug, one drug.” It takes a detailed understanding of a virus to develop an antiviral against it, says Che Colpitts, a virologist at Queen’s University, in Canada, who works on antivirals against RNA viruses. “When a new virus emerges, like SARS-CoV-2, we’re at a big disadvantage.”

Making drugs to stop viruses is hard for three main reasons. First, viruses are the Spartans of the pathogen world: They’re frugal, brutal, and expert at evading the human immune system. About 20 to 250 nanometers in diameter, viruses rely on just a few parts to operate, hijacking host cells to reproduce and often destroying those cells upon departure. They employ tricks to camouflage their presence from the host’s immune system, including preventing infected cells from sending out molecular distress beacons. “Viruses are really small, so they only have a few components, so there’s not that many drug targets available to start with,” says Colpitts.

Second, viruses replicate quickly, typically doubling in number in hours or days. This constant copying of their genetic material enables viruses to evolve quickly, producing mutations able to sidestep drug effects. The virus that causes AIDS soon develops resistance when exposed to a single drug. That’s why a cocktail of antiviral drugs is used to treat HIV infection.

Finally, unlike bacteria, which can exist independently outside human cells, viruses invade human cells to propagate, so any drug designed to eliminate a virus needs to spare the host cell. A drug that fails to distinguish between a virus and a cell can cause serious side effects. “Discriminating between the two is really quite difficult,” says Evotec’s Carter, who has worked in antiviral drug discovery for over three decades.

And then there’s the money barrier. Developing antivirals is rarely profitable. Health-policy researchers at the London School of Economics recently estimated that the average cost of developing a new drug is US $1 billion, and up to $2.8 billion for cancer and other specialty drugs. Because antivirals are usually taken for only short periods of time or during short outbreaks of disease, companies rarely recoup what they spent developing the drug, much less turn a profit, says Carter.

To change the status quo, drug discovery needs fresh approaches that leverage new technologies, rather than incremental improvements, says Christian Tidona, managing director of BioMed X, an independent research institute in Heidelberg, Germany. “We need breakthroughs.”

Putting Drug Development on Autopilot
Earlier this year, SRI Biosciences and Iktos began collaborating on a way to use artificial intelligence and automated chemistry to rapidly identify new drugs to target the COVID-19 virus. Within four months, they had designed and synthesized a first round of antiviral candidates. Here’s how they’re doing it.


STEP 1: Iktos’s AI platform uses deep-learning algorithms in an iterative process to come up with new molecular structures likely to bind to and disable a specific coronavirus protein. Illustrations: Chris Philpot


STEP 2: SRI Biosciences’s SynFini system is a three-part automated chemistry suite for producing new compounds. Starting with a target compound from Iktos, SynRoute uses machine learning to analyze and optimize routes for creating that compound, with results in about 10 seconds. It prioritizes routes based on cost, likelihood of success, and ease of implementation.


STEP 3: SynJet, an automated inkjet printer platform, tests the routes by printing out tiny quantities of chemical ingredients to see how they react. If the right compound is produced, the platform tests it.


STEP 4: AutoSyn, an automated tabletop chemical plant, synthesizes milligrams to grams of the desired compound for further testing. Computer-selected “maps” dictate paths through the plant’s modular components.


STEP 5: The most promising compounds are tested against live virus samples.


Iktos’s AI platform was created by a medicinal chemist and an AI expert. To tackle SARS-CoV-2, the company used generative models—deep-learning algorithms that generate new data—to “imagine” molecular structures with a good chance of disabling a key coronavirus protein.

For a new drug target, the software proposes and evaluates roughly 1 million compounds, says Gaston-Mathé. It’s an iterative process: At each step, the system generates 100 virtual compounds, which are tested in silico with predictive models to see how closely they meet the objectives. The test results are then used to design the next batch of compounds. “It’s like we have a very, very fast chemist who is designing compounds, testing compounds, getting back the data, then designing another batch of compounds,” he says.

The computer isn’t as smart as a human chemist, Gaston-Mathé notes, but it’s much faster, so it can explore far more of what people in the field call “chemical space”—the set of all possible organic compounds. Unexplored chemical space is huge: Biochemists estimate that there are at least 1063 possible druglike molecules, and that 99.9 percent of all possible small molecules or compounds have never been synthesized.

Still, designing a chemical compound isn’t the hardest part of creating a new drug. After a drug candidate is designed, it must be synthesized, and the highly manual process for synthesizing a new chemical hasn’t changed much in 200 years. It can take days to plan a synthesis process and then months to years to optimize it for manufacture.

That’s why Gaston-Mathé was eager to send Iktos’s AI-generated designs to Collins’s team at SRI Biosciences. With $13.8 million from the Defense Advanced Research Projects Agency, SRI Biosciences spent the last four years automating the synthesis process. The company’s automated suite of three technologies, called SynFini, can produce new chemical compounds in just hours or days, says Collins.

First, machine-learning software devises possible routes for making a desired molecule. Next, an inkjet printer platform tests the routes by printing out and mixing tiny quantities of chemical ingredients to see how they react with one another; if the right compound is produced, the platform runs tests on it. Finally, a tabletop chemical plant synthesizes milligrams to grams of the desired compound.

Less than four months after Iktos and SRI Biosciences announced their collaboration, they had designed and synthesized a first round of antiviral candidates for SARS-CoV-2. Now they’re testing how well the compounds work on actual samples of the virus.

Out of 10
63 possible druglike molecules, 99.9 percent have never been synthesized.

Theirs isn’t the only collaborationapplying new tools to drug discovery. In late March, Alex Zhavoronkov, CEO of Hong Kong–based Insilico Medicine, came across a YouTube video showing three virtual-reality avatars positioning colorful, sticklike fragments in the side of a bulbous blue protein. The three researchers were using VR to explore how compounds might bind to a SARS-CoV-2 enzyme. Zhavoronkov contacted the startup that created the simulation—Nanome, in San Diego—and invited it to examine Insilico’s ­AI-generated molecules in virtual reality.

Insilico runs an AI platform that uses biological data to train deep-learning algorithms, then uses those algorithms to identify molecules with druglike features that will likely bind to a protein target. A four-day training sprint in late January yielded 100 molecules that appear to bind to an important SARS-CoV-2 protease. The company recently began synthesizing some of those molecules for laboratory testing.

Nanome’s VR software, meanwhile, allows researchers to import a molecular structure, then view and manipulate it on the scale of individual atoms. Like human chess players who use computer programs to explore potential moves, chemists can use VR to predict how to make molecules more druglike, says Nanome CEO Steve McCloskey. “The tighter the interface between the human and the computer, the more information goes both ways,” he says.

Zhavoronkov sent data about several of Insilico’s compounds to Nanome, which re-created them in VR. Nanome’s chemist demonstrated chemical tweaks to potentially improve each compound. “It was a very good experience,” says Zhavoronkov.

Meanwhile, in March, Takeda Pharmaceutical Co., of Japan, invited Schrödinger, a New York–based company that develops chemical-simulation software, to join an alliance working on antivirals. Schrödinger’s AI focuses on the physics of how proteins interact with small molecules and one another.

The software sifts through billions of molecules per week to predict a compound’s properties, and it optimizes for multiple desired properties simultaneously, says Karen Akinsanya, chief biomedical scientist and head of discovery R&D at Schrödinger. “There’s a huge sense of urgency here to come up with a potent molecule, but also to come up with molecules that are going to be well tolerated” by the body, she says. Drug developers are seeking compounds that can be broadly used and easily administered, such as an oral drug rather than an intravenous drug, she adds.

Schrödinger evaluated four protein targets and performed virtual screens for two of them, a computing-intensive process. In June, Google Cloud donated the equivalent of 16 million hours of Nvidia GPU time for the company’s calculations. Next, the alliance’s drug companies will synthesize and test the most promising compounds identified by the virtual screens.

Other companies, including Amazon Web Services, IBM, and Intel, as well as several U.S. national labs are also donating time and resources to the Covid-19 High Performance Computing Consortium. The consortium is supporting 87 projects, which now have access to 6.8 million CPU cores, 50,000 GPUs, and 600 petaflops of computational resources.

While advanced technologies could transform early drug discovery, any new drug candidate still has a long road after that. It must be tested in animals, manufactured in large batches for clinical trials, then tested in a series of trials that, for antivirals, lasts an average of seven years.

In May, the BioMed X Institute in Germany launched a five-year project to build a Rapid Antiviral Response Platform, which would speed drug discovery all the way through manufacturing for clinical trials. The €40 million ($47 million) project, backed by drug companies, will identify ­outside-the-box proposals from young scientists, then provide space and funding to develop their ideas.

“We’ll focus on technologies that allow us to go from identification of a new virus to 10,000 doses of a novel potential therapeutic ready for trials in less than six months,” says BioMed X’s Tidona, who leads the project.

While a vaccine will likely arrive long before a bespoke antiviral does, experts expect COVID-19 to be with us for a long time, so the effort to develop a direct-acting, potent antiviral continues. Plus, having new antivirals—and tools to rapidly create more—can only help us prepare for the next pandemic, whether it comes next month or in another 102 years.

“We’ve got to start thinking differently about how to be more responsive to these kinds of threats,” says Collins. “It’s pushing us out of our comfort zones.”

This article appears in the October 2020 print issue as “Automating Antivirals.” Continue reading

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