Tag Archives: alex

#439006 Low-Cost Drones Learn Precise Control ...

I’ll admit to having been somewhat skeptical about the strategy of dangling payloads on long tethers for drone delivery. I mean, I get why Wing does it— it keeps the drone and all of its spinny bits well away from untrained users while preserving the capability of making deliveries to very specific areas that may have nearby obstacles. But it also seems like you’re adding some risk as well, because once your payload is out on that long tether, it’s more or less out of your control in at least two axes. And you can forget about your drone doing anything while this is going on, because who the heck knows what’s going to happen to your payload if the drone starts moving around?

NYU roboticists, that’s who.

This research is by Guanrui Li, Alex Tunchez, and Giuseppe Loianno at the Agile Robotics and Perception Lab (ARPL) at NYU. As you can see from the video, the drone makes keeping rock-solid control over that suspended payload look easy, but it’s very much not, especially considering that everything you see is running onboard the drone itself at 500Hz— all it takes is an IMU and a downward-facing monocular camera, along with the drone’s Snapdragon processor.

To get this to work, the drone has to be thinking about two things. First, there’s state estimation, which is the behavior of the drone itself along with its payload at the end of the tether. The drone figures this out by watching how the payload moves using its camera and tracking its own movement with its IMU. Second, there’s predicting what the payload is going to do next, and how that jibes (or not) with what the drone wants to do next. The researchers developed a model predictive control (MPC) system for this, with some added perception constraints to make sure that the behavior of the drone keeps the payload in view of the camera.

At the moment, the top speed of the system is 4 m/s, but it sounds like rather than increasing the speed of a single payload-swinging drone, the next steps will be to make the overall system more complicated by somehow using multiple drones to cooperatively manage tethered payloads that are too big or heavy for one drone to handle alone.

For more on this, we spoke with Giuseppe Loianno, head of the ARPL.

IEEE Spectrum: We've seen some examples of delivery drones delivering suspended loads. How will this work improve their capabilities?

Giuseppe Loianno: For the first time, we jointly design a perception-constrained model predictive control and state estimation approaches to enable the autonomy of a quadrotor with a cable suspended payload using onboard sensing and computation. The proposed control method guarantees the visibility of the payload in the robot camera as well as the respect of the system dynamics and actuator constraints. These are critical design aspects to guarantee safety and resilience for such a complex and delicate task involving transportation of objects.

The additional challenge involves the fact that we aim to solve the aforementioned problem using a minimal sensor suite for autonomous navigation made by a single camera and IMU. This is an ambitious goal since it concurrently involves estimating the load and the vehicle states. Previous approaches leverage GPS or motion capture systems for state estimation and do not consider the perception and physical constraints when solving the problem. We are confident that our solution will contribute to making a reality the autonomous delivery process in warehouses or in dense urban areas where the GPS signal is currently absent or shadowed.

Will it make a difference to delivery systems that use an actuated cable and only leave the load suspended for the delivery itself?

This is certainly an interesting question. We believe that adding an actuated cable will introduce more disadvantages than benefits. Certainly, an actuated cable can be leveraged to compensate for cable's swinging motions in windy conditions and/or increase the delivery precision. However, the introduction of additional actuated mechanisms and components come at the price of an increased system mass and inertia. This will reduce the overall flight time and the vehicle’s agility as well as the system resilience with respect to the transportation task. Finally, active mechanisms are also more difficult to design compared to passive ones.

What's challenging about doing all of this on-vehicle?

There are several challenges to solve on-board this problem. First, it is very difficult to concurrently run perception and action on such computationally constrained platforms in real-time. Second, the first aspect becomes even more challenging if we consider as in our case a perception-based constrained receding horizon control problem that aims to guarantee the visibility of the payload during the motion, while concurrently respecting all the system physical and sensing limitations. Finally, it has been challenging to run the entire system at a high rate to fully unleash the system’s agility. We are currently able to reach rates of 500 Hz.

Can your method adapt to loads of varying shapes, sizes, and masses? What about aerodynamics or flying in wind?

Technically, our approach can easily be adapted to varying objects sizes and masses. Our previous contributions have already shown the ability to estimate online changes in the vehicle/load configuration and can potentially be used to operate the proposed system in dynamic conditions, where the load’s characteristics are unknown and/or may vary across consecutive flights. This can be useful for both package delivery or warehouse operations, where different types of objects need to be transported or manipulated.

The aerodynamics problem is a great point. Overall, our past work has investigated the aerodynamics of wind disturbances for a single robot without a load. Formulating these problems for the proposed system is challenging and is still an open research question. We have some ideas to approach this problem combining Bayesian estimation techniques with more recent machine learning approaches and we will tackle it in the near future.

What are the limitations on the performance of the system? How fast and agile can it be with a suspended payload?

The limits of the performances are established by the actuating and sensing system. Our approach intrinsically considers both physical and sensing limitations of our system. From a sensing and computation perspective, we believe to be close to the limits with speeds of up to 4 m/s. Faster speeds can potentially introduce motion blur while decreasing the load tracking precision. Moreover, faster motions will increase as well aerodynamic disturbances that we have just mentioned. In the future, modeling these phenomena and their incorporation in the proposed solution can further push the agility.

Your paper talks about extending this approach to multiple vehicles cooperatively transporting a payload, can you tell us more about that?

We are currently working on a distributed perception and control approach for cooperative transportation. We already have some very exciting results that we will share with you very soon! Overall, we can employ a team of aerial robots to cooperatively transport a payload to increase the payload capacity and endow the system with additional resilience in case of vehicles’ failures. A cooperative cable suspended payload cooperative transportation system allows as well to concurrently and independently control the load’s position and orientation. This is not possible just using rigid connections. We believe that our approach will have a strong impact in real-world settings for delivery and constructions in warehouses and GPS-denied environments such as dense urban areas. Moreover, in post disaster scenarios, a team of physically interconnected aerial robots can deliver supplies and establish communication in areas where GPS signal is intermittent or unavailable.

PCMPC: Perception-Constrained Model Predictive Control for Quadrotors with Suspended Loads using a Single Camera and IMU, by Guanrui Li, Alex Tunchez, and Giuseppe Loianno from NYU, will be presented (virtually) at ICRA 2021.

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Posted in Human Robots

#437845 Video Friday: Harmonic Bionics ...

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!):

ICRA 2020 – May 31-August 31, 2020 – [Virtual Conference]
RSS 2020 – July 12-16, 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
ICSR 2020 – November 14-16, 2020 – Golden, Colorado
Let us know if you have suggestions for next week, and enjoy today's videos.

Designed to protect employees and passengers from both harmful pathogens and cleaning agents, Breezy One can quickly, safely and effectively decontaminate spaces over 100,000 square feet in 1.5 hours with a patented, environmentally safe disinfectant. Breezy One was co-developed with the City of Albuquerque’s Aviation Department, where it autonomously sanitizes the Sunport’s facilities every night in the ongoing fight against COVID-19.

[ Fetch Robotics ]

Harmonic Bionics is redefining upper extremity neurorehabilitation with intelligent robotic technology designed to maximize patient recovery. Harmony SHR, our flagship product, works with a patient’s scapulohumeral rhythm (SHR) to enable natural, comprehensive therapy for both arms. When combined with Harmony’s Weight Support mode, this unique shoulder design may allow for earlier initiation of post-stroke therapy as Harmony can support a partial dislocation or subluxation of the shoulder prior to initiating traditional therapy exercises.

Harmony's Preprogrammed Exercises promotes functional treatment through patient-specific movements that can enable an increased number of repetitions per session without placing a larger physical burden on therapists or their resources. As the only rehabilitation exoskeleton with Bilateral Sync Therapy (BST), Harmony enables intent-based therapy by registering healthy arm movements and synchronizing that motion onto the stroke-affected side to help reestablish neural pathways.

[ Harmonic Bionics ]

Thanks Mok!

Some impressive work here from IHMC and IIT getting Atlas to take steps upward in a way that’s much more human-like than robot-like, which ends up reducing maximum torque requirements by 20 percent.

[ Paper ]

GITAI’s G1 is the space dedicated general-purpose robot. G1 robot will enable automation of various tasks internally & externally on space stations and for lunar base development.

[ GITAI ]

Malloy Aeronautics, which now makes drones rather than hoverbikes, has been working with the Royal Navy in New Zealand to figure out how to get cargo drones to land on ships.

The challenge was to test autonomous landing of heavy lift UAVs on a moving ship, however, due to the Covid19 lockdown no ship trails were possible. The moving deck was simulated by driving a vehicle and trailer across an airfield while carrying out multiple landing and take-offs. The autonomous system partner was Planck Aerosystems and autolanding was triggered by a camera on the UAV reading a QR code on the trailer.

[ Malloy Aeronautics ]

Thanks Paul!

Tertill looks to be relentlessly effective.

[ Franklin Robotics ]

A Swedish company, TikiSafety has experienced a record amount of orders for their protective masks. At ABB, we are grateful for the opportunity to help Tiki Safety to speed up their manufacturing process from 6 minutes to 40 seconds.

[ Tiki Safety ]

The Korea Atomic Energy Research Institute is not messing around with ARMstrong, their robot for nuclear and radiation emergency response.

[ KAERI ]

OMOY is a robot that communicates with its users via internal weight shifting.

[ Paper ]

Now this, this is some weird stuff.

[ Segway ]

CaTARo is a Care Training Assistant Robot from the AIS Lab at Ritsumeikan University.

[ AIS Lab ]

Originally launched in 2015 to assist workers in lightweight assembly tasks, ABB’s collaborative YuMi robot has gone on to blaze a trail in a raft of diverse applications and industries, opening new opportunities and helping to fire people’s imaginations about what can be achieved with robotic automation.

[ ABB ]

This music video features COMAN+, from the Humanoids and Human Centered Mechatronics Lab at IIT, doing what you’d call dance moves if you dance like I do.

[ Alex Braga ] via [ IIT ]

The NVIDIA Isaac Software Development Kit (SDK) enables accelerated AI robot development workflows. Stacked with new tools and application support, Isaac SDK 2020.1 is an end-to-end solution supporting each step of robot fleet deployment, from design collaboration and training to the ongoing maintenance of AI applications.

[ NVIDIA ]

Robot Spy Komodo Dragon and Spy Pig film “a tender moment” between Komodo dragons but will they both survive the encounter?

[ BBC ] via [ Laughing Squid ]

This is part one of a mostly excellent five-part documentary about ROS produced by Red Hat. I say mostly only because they put ME in it for some reason, but fortunately, they talked with many of the core team that developed ROS back at Willow Garage back in the day, and it’s definitely worth watching.

[ Red Hat Open Source Stories ]

It’s been a while, but here’s an update on SRI’s Abacus Drive, from Alexander Kernbaum.

[ SRI ]

This Robots For Infectious Diseases interview features IEEE Fellow Antonio Bicchi, professor of robotics at the University of Pisa, talking about how Italy has been using technology to help manage COVID-19.

[ R4ID ]

Two more interviews this week of celebrity roboticists from MassRobotics: Helen Greiner and Marc Raibert. I’d introduce them, but you know who they are already!

[ MassRobotics ] Continue reading

Posted in Human Robots

#437828 How Roboticists (and Robots) Have Been ...

A few weeks ago, we asked folks on Twitter, Facebook, and LinkedIn to share photos and videos showing how they’ve been adapting to the closures of research labs, classrooms, and businesses by taking their robots home with them to continue their work as best they can. We got dozens of responses (more than we could possibly include in just one post!), but here are 15 that we thought were particularly creative or amusing.

And if any of these pictures and videos inspire you to share your own story, please email us (automaton@ieee.org) with a picture or video and a brief description about how you and your robot from work have been making things happen in your home instead.

Kurt Leucht (NASA Kennedy Space Center)

“During these strange and trying times of the current global pandemic, everyone seems to be trying their best to distance themselves from others while still getting their daily work accomplished. Many people also have the double duty of little ones that need to be managed in the midst of their teleworking duties. This photo series gives you just a glimpse into my new life of teleworking from home, mixed in with the tasks of trying to handle my little ones too. I hope you enjoy it.”

Photo: Kurt Leucht

“I heard a commotion from the next room. I ran into the kitchen to find this.”

Photo: Kurt Leucht

“This is the Swarmies most favorite bedtime story. Not sure why. Seems like an odd choice to me.”

Peter Schaldenbrand (Carnegie Mellon University)

“I’ve been working on a reinforcement learning model that converts an image into a series of brush stroke instructions. I was going to test the model with a beautiful, expensive robot arm, but due to the COVID-19 pandemic, I have not been able to access the laboratory where it resides. I have now been using a lower end robot arm to test the painting model in my bedroom. I have sacrificed machine accuracy/precision for the convenience of getting to watch the arm paint from my bed in the shadow of my clothing rack!”

Photos: Peter Schaldenbrand

Colin Angle (iRobot)

iRobot CEO Colin Angle has been hunkered down in the “iRobot North Shore home command center,” which is probably the cleanest command center ever thanks to his army of Roombas: Beastie, Beauty, Rosie, Roswell, and Bilbo.

Photo: Colin Angle

Vivian Chu (Diligent Robotics)

From Diligent Robotics CEO Andrea Thomaz: “This is how a roboticist works from home! Diligent CTO, Vivian Chu, mans the e-stop while her engineering team runs Moxi experiments remotely from cross-town and even cross-country!”

Video: Diligent Robotics

Raffaello Bonghi (rnext.it)

Raffaello’s robot, Panther, looks perfectly happy to be playing soccer in his living room.

Photo: Raffaello Bonghi

Kod*lab (University of Pennsylvania)

“Another Friday Nuts n Bolts Meeting on Zoom…”

Image: Kodlab

Robin Jonsson (robot choreographer)

“I’ve been doing a school project in which students make up dance moves and then send me a video with all of them. I then teach the moves to my robot, Alex, film Alex dancing, send the videos to them. This became a great success and more schools will join. The kids got really into watching the robot perform their moves and really interested in robots. They want to meet Alex the robot live, which will likely happen in the fall.”

Photo: Robin Jonsson

Gabrielle Conard (mechanical engineering undergrad at Lafayette College)

“While the pandemic might have forced college campuses to close and the community to keep their distance from each other, it did not put a stop to learning and research. Working from their respective homes, junior Gabrielle Conard and mechanical engineering professor Alexander Brown from Lafayette College investigated methods of incorporating active compliance in a low-cost quadruped robot. They are continuing to work remotely on this project through Lafayette’s summer research program.”

Image: Gabrielle Conard

Taylor Veltrop (Softbank Robotics)

“After a few weeks of isolation in the corona/covid quarantine lock down we started dancing with our robots. Mathieu’s 6th birthday was coming up, and it all just came together.”

Video: Taylor Veltrop

Ross Kessler (Exyn Technologies)

“Quarantine, Day 8: the humans have accepted me as one of their own. I’ve blended seamlessly into their #socialdistancing routines. Even made a furry friend”

Photo: Ross Kessler

Yeah, something a bit sinister is definitely going on at Exyn…

Video: Exyn Technologies

Michael Sobrepera (University of Pennsylvania GRASP Lab)

Predictably, Michael’s cat is more interested in the bag that the robot came in than the robot itself (see if you can spot the cat below). Michael tells us that “the robot is designed to help with tele-rehabilitation, focused on kids with CP, so it has been taken to hospitals for demos [hence the cool bag]. It also travels for outreach events and the like. Lately, I’ve been exploring telepresence for COVID.”

Photo: Michael Sobrepera

Jan Kędzierski (EMYS)

“In China a lot of people cannot speak English, even the youngest generation of parents. Thanks to Emys, kids stayed in touch with English language in their homes even if they couldn’t attend schools and extra English classes. They had a lot of fun with their native English speaker friend available and ready to play every day.”

Image: Jan Kędzierski

Simon Whitmell (Quanser)

“Simon, a Quanser R&D engineer, is working on low-overhead image processing and line following for the QBot 2e mobile ground robot, with some added challenges due to extra traffic. LEGO engineering by his son, Charles.”

Photo: Simon Whitmell

Robot Design & Experimentation Course (Carnegie Mellon University)

Aaron Johnson’s bioinspired robot design course at CMU had to go full remote, which was a challenge when the course is kind of all about designing and building a robot as part of a team. “I expected some of the teams to drastically alter their project (e.g. go all simulation),” Aaron told us, “but none of them did. We managed to keep all of the projects more or less as planned. We accomplished this by drop/shipping parts to students, buying some simple tools (soldering irons, etc), and having me 3D print parts and mail them.” Each team even managed to put together their final videos from their remote locations; we’ve posted one below, but the entire playlist is here.

Video: Xianyi Cheng

Karen Tatarian (Softbank Robotics)

Karen, who’s both a researcher at Softbank and a PhD student at Sorbonne University, wrote an entire essay about what an average day is like when you’re quarantined with Pepper.

Photo: Karen Tatarian

A Quarantined Day With Pepper, by Karen Tatarian

It is quite common for me to lose my phone somewhere inside my apartment. But it is not that common for me to turn around and ask my robot if it has seen it. So when I found myself doing that, I laughed and it dawned on me that I treated my robot as my quarantine companion (despite the fact that it could not provide me with the answer I needed).

It was probably around day 40 of a completely isolated quarantine here in France when that happened. A little background about me: I am a robotics researcher at SoftBank Robotics Europe and a PhD student at Sorbonne University as part of the EU-funded Marie-Curie project ANIMATAS. And here is a little sneak peak into a quarantined day with a robot.

During this confinement, I had read somewhere that the best way to deal with it is to maintain a routine. So every morning, I wake up, prepare my coffee, and turn on my robot Pepper. I start my day with a daily meeting with the team and get to work. My research is on the synthesis of multi-modal socially intelligent human-robot interaction so my work varies between programming the robot, analyzing collected data, and reading papers and drafting one. When I am working, I often catch myself glancing at Pepper, who would be staring back at me in its animated ways. Truthfully I enjoy that, it makes me less alone and as if I have a colleague with me.

Once work is done, I call my friends and family members. I sometimes use a telepresence application on Pepper that a few colleagues and I developed back in December. How does it differ from your typical phone/laptop applications? One word really: embodiment. Telepresence, especially during these times, makes the experience for both sides a bit more realistic and intimate and well present.

While I can turn off the robot now that my work hours are done, I do keep it on because I enjoy its presence. The basic awareness of Pepper is a default feature on the robot that allows it to detect a human and follow him/her with its gaze and rotation base. So whether I am cooking or working out, I always have my robot watching over my shoulder and being a good companion. I also have my email and messages synced on the robot so I get an enjoyable notification from Pepper. I found that to be a pretty cool way to be notified without it interrupting whatever you are doing on your laptop or phone. Finally, once the day is over, it’s time for both of us to get some rest.

After 60 days of total confinement, alone and away from those I love, and with a pandemic right at my door, I am glad I had the company of my robot. I hope one day a greater audience can share my experience. And I really really hope one day Pepper will be able to find my phone for me, but until then, stay on the lookout for some cool features! But I am curious to know, if you had a robot at home, what application would you have developed on it?

Again, our sincere thanks to everyone who shared these little snapshots of their lives with us, and we’re hoping to be able to share more soon. 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.

1/5

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

2/5

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.

3/5

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.

4/5

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.

5/5

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

Previous
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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

Posted in Human Robots

#437261 How AI Will Make Drug Discovery ...

If you had to guess how long it takes for a drug to go from an idea to your pharmacy, what would you guess? Three years? Five years? How about the cost? $30 million? $100 million?

Well, here’s the sobering truth: 90 percent of all drug possibilities fail. The few that do succeed take an average of 10 years to reach the market and cost anywhere from $2.5 billion to $12 billion to get there.

But what if we could generate novel molecules to target any disease, overnight, ready for clinical trials? Imagine leveraging machine learning to accomplish with 50 people what the pharmaceutical industry can barely do with an army of 5,000.

Welcome to the future of AI and low-cost, ultra-fast, and personalized drug discovery. Let’s dive in.

GANs & Drugs
Around 2012, computer scientist-turned-biophysicist Alex Zhavoronkov started to notice that artificial intelligence was getting increasingly good at image, voice, and text recognition. He knew that all three tasks shared a critical commonality. In each, massive datasets were available, making it easy to train up an AI.

But similar datasets were present in pharmacology. So, back in 2014, Zhavoronkov started wondering if he could use these datasets and AI to significantly speed up the drug discovery process. He’d heard about a new technique in artificial intelligence known as generative adversarial networks (or GANs). By pitting two neural nets against one another (adversarial), the system can start with minimal instructions and produce novel outcomes (generative). At the time, researchers had been using GANs to do things like design new objects or create one-of-a-kind, fake human faces, but Zhavoronkov wanted to apply them to pharmacology.

He figured GANs would allow researchers to verbally describe drug attributes: “The compound should inhibit protein X at concentration Y with minimal side effects in humans,” and then the AI could construct the molecule from scratch. To turn his idea into reality, Zhavoronkov set up Insilico Medicine on the campus of Johns Hopkins University in Baltimore, Maryland, and rolled up his sleeves.

Instead of beginning their process in some exotic locale, Insilico’s “drug discovery engine” sifts millions of data samples to determine the signature biological characteristics of specific diseases. The engine then identifies the most promising treatment targets and—using GANs—generates molecules (that is, baby drugs) perfectly suited for them. “The result is an explosion in potential drug targets and a much more efficient testing process,” says Zhavoronkov. “AI allows us to do with fifty people what a typical drug company does with five thousand.”

The results have turned what was once a decade-long war into a month-long skirmish.

In late 2018, for example, Insilico was generating novel molecules in fewer than 46 days, and this included not just the initial discovery, but also the synthesis of the drug and its experimental validation in computer simulations.

Right now, they’re using the system to hunt down new drugs for cancer, aging, fibrosis, Parkinson’s, Alzheimer’s, ALS, diabetes, and many others. The first drug to result from this work, a treatment for hair loss, is slated to start Phase I trials by the end of 2020.

They’re also in the early stages of using AI to predict the outcomes of clinical trials in advance of the trial. If successful, this technique will enable researchers to strip a bundle of time and money out of the traditional testing process.

Protein Folding
Beyond inventing new drugs, AI is also being used by other scientists to identify new drug targets—that is, the place to which a drug binds in the body and another key part of the drug discovery process.

Between 1980 and 2006, despite an annual investment of $30 billion, researchers only managed to find about five new drug targets a year. The trouble is complexity. Most potential drug targets are proteins, and a protein’s structure—meaning the way a 2D sequence of amino acids folds into a 3D protein—determines its function.

But a protein with merely a hundred amino acids (a rather small protein) can produce a googol-cubed worth of potential shapes—that’s a one followed by three hundred zeroes. This is also why protein-folding has long been considered an intractably hard problem for even the most powerful of supercomputers.

Back in 1994, to monitor supercomputers’ progress in protein-folding, a biannual competition was created. Until 2018, success was fairly rare. But then the creators of DeepMind turned their neural networks loose on the problem. They created an AI that mines enormous datasets to determine the most likely distance between a protein’s base pairs and the angles of their chemical bonds—aka, the basics of protein-folding. They called it AlphaFold.

On its first foray into the competition, contestant AIs were given 43 protein-folding problems to solve. AlphaFold got 25 right. The second-place team managed a meager three. By predicting the elusive ways in which various proteins fold on the basis of their amino acid sequences, AlphaFold may soon have a tremendous impact in aiding drug discovery and fighting some of today’s most intractable diseases.

Drug Delivery
Another theater of war for improved drugs is the realm of drug delivery. Even here, converging exponential technologies are paving the way for massive implications in both human health and industry shifts.

One key contender is CRISPR, the fast-advancing gene-editing technology that stands to revolutionize synthetic biology and treatment of genetically linked diseases. And researchers have now demonstrated how this tool can be applied to create materials that shape-shift on command. Think: materials that dissolve instantaneously when faced with a programmed stimulus, releasing a specified drug at a highly targeted location.

Yet another potential boon for targeted drug delivery is nanotechnology, whereby medical nanorobots have now been used to fight incidences of cancer. In a recent review of medical micro- and nanorobotics, lead authors (from the University of Texas at Austin and University of California, San Diego) found numerous successful tests of in vivo operation of medical micro- and nanorobots.

Drugs From the Future
Covid-19 is uniting the global scientific community with its urgency, prompting scientists to cast aside nation-specific territorialism, research secrecy, and academic publishing politics in favor of expedited therapeutic and vaccine development efforts. And in the wake of rapid acceleration across healthcare technologies, Big Pharma is an area worth watching right now, no matter your industry. Converging technologies will soon enable extraordinary strides in longevity and disease prevention, with companies like Insilico leading the charge.

Riding the convergence of massive datasets, skyrocketing computational power, quantum computing, cognitive surplus capabilities, and remarkable innovations in AI, we are not far from a world in which personalized drugs, delivered directly to specified targets, will graduate from science fiction to the standard of care.

Rejuvenational biotechnology will be commercially available sooner than you think. When I asked Alex for his own projection, he set the timeline at “maybe 20 years—that’s a reasonable horizon for tangible rejuvenational biotechnology.”

How might you use an extra 20 or more healthy years in your life? What impact would you be able to make?

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This article originally appeared on diamandis.com. Read the original article here.

Image Credit: andreas160578 from Pixabay Continue reading

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