Artificial Intelligence

April 5, 2021 6 min read

Opinions expressed by Entrepreneur contributors are their own.

At a Fintech conference in put on by Fordham University in the spring of 2017, an AI expert made a bold prediction: Someday there would be a company with a market cap of one trillion dollars. He predicted that this valuation, which at the time seemed incredible, would be based on that firm’s extensive use of AI.

He was correct in at least one regard: became the world’s first trillion-dollar company a little over a year later. But what of the second part of the prediction? Was Apple’s staggering valuation due to the power of AI? Are AI and, more broadly, , the key drivers of business growth?

Apple uses data analytics and AI extensively. combines speech recognition and expert systems to give you reminders based on your location. studies your listening habits and assembles playlists accordingly. Apple Fitness+ uses data from the Apple watch to help users build health. In 2018, Apple’s head of AI, John Giannandrea, was appointed to the company’s executive team.

Yet the same press release announcing Giannandrea’s appointment offers a fundamental insight into why Apple has been so successful. It notes that Apple “leads the world in innovation” — not AI. The company has spent decades creating entirely new product arenas and pioneering new business models around music sales (), app subscriptions (the ), cloud storage (), and digital payments (Apple Pay). It’s easy to forget that just 20 years ago, computers made up nearly all of Apple’s business. Last year, Mac products contributed just 10.4% of the company’s revenue.

Human creativity is visionary in ways that AI can’t be

Apple’s new products and business models relied on creativity and the ability to see beyond the known, not or AI. Creative leaders like and could see the deficiencies in portable MP3 players, but no algorithm could have told them how to build an entirely new way to listen to music. The iPod, the , and the iPad emerged from their ability to envision ways to apply new technologies and their outstanding sense of user-centric design. Services like iTunes and the App Store stemmed from the company’s commitment to provide entirely new and valuable experiences for consumers. Innovation, not AI or analytics, generated the returns that drive Apple’s trillion-dollar valuation.

Related: Machine Learning and Artificial Intelligence to Revolutionize the …

Nor could data be expected to produce such results. Quantitative techniques, even sophisticated ones like deep learning, are backward-looking, highly constrained, and reductionist. They begin with an established dataset and seek the best answer from a limited number of pre-defined choices. The very nature of data-driven tools makes them unsuited to coming up with bold, disruptive innovations — the kind of breakthroughs that lead to something entirely new. For example, one of the most promising AI technologies at the moment is the GPT-3 “few-shot” learning model, which has shown a modest ability to do some creative tasks like generating synthetic news articles and computer code. Today, though, its domain is primarily limited to natural language processing — and even that uses a model with 175 billion parameters.

Breakthrough innovation occurs because of ideas sparked by a serendipitous conversation, an unexpected finding in a lab, or the ability to connect disparate pieces of information from very different domains into a keen insight. When Jony Ive was hired at Apple, he had designed products ranging from telephones to toilets but had done nothing in the computer industry. Yet he was at the heart of Apple’s new product successes for two decades because of his ability to envision how new technologies could build a world that did not yet exist.

Reliance on AI can actually hurt more than it helps

The danger companies today face is that an over-emphasis on AI and quantitative tools can potentially hinder breakthrough innovation. If each decision must be driven by data, how will a firm create something for which there is no relevant data? No algorithm can justify investing in and launching a breakthrough innovation.

Related: How Artificial Intelligence Is Helping Fight The COVID-19 Pandemic

Moreover, a firm’s bandwidth for business improvement can be consumed by the use of analytical tools. In such cases, incremental innovation will rule the day. Renowned computer scientist Melanie Mitchell summarizes the trap that businesses fall prey to: “The race to commercialize AI has put enormous pressure on researchers to produce systems that work ‘well enough’ on narrow tasks.” Quantitative tools are powerful and exciting, but they can come to dominate a firm, keeping it focused on narrow tasks to the detriment of breakthrough innovation.

This isn’t to say that there is no role for data analysis. Data tools are enabling technologies that can improve and extend existing products. Firms should use AI technologies within their breakthrough innovations. For example, while there is no algorithm that could have taken mobile phone data in the early 2000s and come up with the iPhone, the AI-driven assistant Siri added value to this breakthrough innovation. AI is one of a number of functional areas at Apple, where it sits alongside software, hardware engineering, design, and other business areas.

The importance of breakthrough innovation is further illustrated by the other members of the trillion-dollar club. Amazon, Alphabet, and Microsoft built their success not on AI or big data, but on breakthrough innovations that transformed the way we shop, work, and consume information. These companies have benefited substantially from analytical capabilities, but they are largely selling products that sprang from human .

The surest path to success isn’t through incremental advances, but breakthrough innovations. While a trillion-dollar firm built on AI may someday rise, it’s not here yet. Companies should not let the shininess of AI and big data distract them from the importance of human processes like creativity and discovery to unlocking breakthrough innovation.

Related: What Is Artificial Intelligence? Whether You’re a Student …

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When IBM first launched its Watson Health unit in 2015, it had to live up to a grandiose vision. The company’s AI creation, popularized by its win on Jeopardy!, was pitched to oncologists as a tool that could comb through medical literature and cancer patients’ health records, detecting patterns that they could not.

Reports later found that it fell short of those claims, and in some cases, offered ‘unsafe and incorrect’ suggestions. Now, IBM might be considering selling its Watson Health unit, as it focuses more on its cloud computing business, according to the Wall Street Journal. Citing anonymous sources, the Journal reported the health unit brought in $1 billion in revenue and isn’t currently profitable.

IBM declined to comment on the report, and it’s not clear who would buy the company. But it’s another example of a high-flying healthcare effort that might have tried to do too much all at once, and a case where marketing overtook the science.

Other tech behemoths have also stumbled in their much-vaunted plans to disrupt healthcare. Haven, a joint venture between Amazon, JPMorgan Chase and Berkshire Hathaway to combat rising healthcare costs, dissolved as the three companies pursued their own efforts.

Part of the challenge is that it’s difficult for these large companies to move quickly, while in the same span, dozens of startups are bringing their own solutions to market.

“There’s a contextual dynamic that large companies will by definition not move as quickly as early-stage innovative companies,” said Michael Greeley, co-founder and general partner with Flare Capital Partners. “A product roadmap that a big tech company might set for the end of the year, by the time committees meet and budget, the year has gone by.”

Two years ago, IBM started winding down sales of Watson for Drug Discovery to pharmaceutical companies, because it wasn’t yielding big enough financial returns. Before that, the general manager of the division also stepped down for a different role at the company.

Facing declining revenues, in an investor call last month, the company’s new CEO, Arvind Krishna, said he was looking to redefine IBM’s future as a cloud platform and AI company.

“This is where we are focusing the bulk of our efforts, time and investments,” he said.

Over-hyped and under-delivered

With the way IBM had marketed Watson for Oncology, “There was clearly always a mismatch in the reality and the promise of what they were going to bring to market,” Greeley said.

More time would have been needed to get closer to that goal. Building AI tools for healthcare requires a huge amount of high-quality data that can be hard to get, and complicated to analyze.

“To date, there’s been far more heat than light,” wrote David Shaywitz, founder of health-tech advisory firm Astounding HealthTech. “There’s a lot of complexity to health data that requires domain expertise to understand, and just sticking a lot of values in a data lake or data swamp and then setting algorithms loose on it hasn’t proved especially productive to date.”

Despite that, Shaywitz still remains optimistic that AI will have a role in medicine in drug development in the future. He pointed to Flatiron Health as an example of one startup that has done well – it was acquired by Roche in 2018 for $1.9 billion.

He said that success relies on the ability for health and tech experts to collaborate as equal partners,  something that’s “vanishingly rare” at big tech, biopharma and healthcare companies.

 Whatever happens with Watson, Greeley still doesn’t see tech companies’ interest in healthcare waning anytime soon, as Amazon wades into the prescription drug market and Google tries to woo more health systems with cloud partnerships.

“I think we’re seeing renewed intrigue by consumer tech, the Googles and Facebooks of the world,” he said. “I think because healthcare is such an important part of the economy, they will continue to be active with acquisitions.”

Photo credit: Getty Images, wigglestick

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Humana and IBM Watson Health are collaborating to provide the insurer’s Employer Group members with access to a conversational AI solution.

The solution, called the IBM Watson Assistant for Health Benefits, is an AI-enabled, cloud-based virtual assistant. The AI assistant gives users information about member benefits, coverage, claims, referrals and healthcare cost estimates, said an IBM Watson Health spokeswomen, who declined to be named, in an email.

The solution will be made available to all members of the Louisville, Kentucky-based payer’s Employer Group, which includes 1.3 million medical and 1.8 million dental members.

IBM is not the only tech giant that is using AI chatbot technology to make inroads in healthcare. Microsoft, for example, has been popular among insurers and providers alike, launching triage chatbots and other AI technology. For IBM, it also affords the chance to prove its value in offering AI services dedicated to healthcare — it stumbled in 2017 in its loftier vision to use the technology to one day revolutionize cancer care and more recently in 2019 when it abandoned the AI product meant to speed up drug discovery.

But Humana believes that IBM Watson Health’s AI assistant will provide several benefits to health plan members, said a spokesman for the insurer, who declined to be named, in an email. Specifically, it will provide personalized answers to questions from members.

“Customers want us to make it easy, meet on their terms, and save them time,” he said. “The Watson [Assistant for Health Benefits] offers immediate answers to the majority of customer questions without [them] having to call in for help.”

In addition, the solution can aid in the move toward price transparency, which is now a part of federal regulations for insurers. Beginning Jan. 1, 2023, insurers must disclose negotiated rates and provide estimates of patient out-of-pocket costs for 500 services and items per a federal rule finalized in October. Payers must make that information publicly available for all items and services starting Jan. 1, 2024.

The IBM AI assistant’s cost transparency tool, which uses historical claims and provider data to calculate cost estimates for members, will help the insurer comply with the federal rule.

This is not the first time Humana and Armonk, New York-based IBM Watson Health have partnered on AI technology. The companies developed the Provider Services Conversational Voice Agent with Watson, which was made available to healthcare providers in 2019.

“Given the success, both parties see considerable value in investing in the co-creation of a new, cloud-native, healthcare-specific product,” said the Humana spokesman. “IBM has the technical experience to optimize the AI platform and Humana has the business expertise to bring forward the desired customer experiences.”

The collaboration between the payer and technology company is coming as the use of AI chatbots is soaring.

Though interest in AI-powered digital assistants was growing prior to 2020, the Covid-19 pandemic accelerated its use. Companies, like chatbot and voice bot company Syllable, found themselves overwhelmed by demand, and health systems like Cincinnati Children’s Hospital Medical Center and Springfield, Illinois-based Memorial Health System quickly developed and deployed the technology.

AI-powered chatbot use is expected to grow and continue to shape healthcare in 2021.

Photo: Gerasimov174, Getty Images

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Insightin Health, a company that offers payers a personalized member engagement platform, has raised $12 million in a Series A funding round.

The round was co-led by the Blue Venture Fund — a collaboration between Blue Cross Blue Shield companies, the Blue Cross Blue Shield Association and Sandbox — and healthcare growth equity fund Blue Heron Capital.

Insightin Health plans to use the new funds to scale its proprietary inGAGE platform, which is designed to help payers retain and engage their members, said Enam Noor, CEO of Insightin Health, in an email.

The platform applies natural language processing to clinical information and social determinants of health and then generates an algorithm for care management and retention for each member, he explained.

“The AI-driven approach and machine learning algorithm connects the dots for all of the data sources and aggregates the trends for predictive outcomes,” he said. “[The platform makes] recommendations for every touchpoint of member communications and activities.”

Currently, the platform has aggregated data for more than 5 million people.

Scaling the inGAGE platform will allow Insightin Health to bring in new clients and provide new tools within the platform for existing customers.

“[Blue Venture Fund is] committed as an organization to improve the health system, and we feel a solution like Insightin Health takes great strides in getting us there,” said Binoy Bhansali, managing director of the Blue Venture Fund, in a news release. “Their ability to leverage data strategically elevates Medicare Advantage and Managed Medicaid plans’ abilities to improve quality, reduce costs, and drive growth and retention by better engaging members.”

Other investors in the funding round included Health Catalyst Capital, Revolution’s Rise of the Rest fund and SaaS Ventures.

The market for personalized consumer engagement platforms is crowded, with companies like Zipari, Welltok and Health Mine all jostling for market share. In addition, large enterprise companies are moving into the healthcare space, such as SalesForce and Microsoft, Noor said.

But Insightin Health sets itself apart by being the only single platform that is able to aggregate all data points for both structured and unstructured data into one ecosystem, he added.

In contrast to many companies that have suffered amid the Covid-19 pandemic, Insightin Health has fared well.

Though Noor declined to share the company’s current valuation, he said Insightin Health experienced 170% revenue growth year-over-year from 2019 to 2020. The company is aiming for similar growth this year.

The company’s growth during the pandemic is partly due to the fact that Insightin Health developed tools focused on the senior population in March 2020, Noor said. These tools gather data on Medicare and Medicaid beneficiaries and then use machine learning to help payers determine patient risk factors.

Photo: StockFinland, Getty Images

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UPMC Enterprises, the innovation and commercialization arm of Pittsburgh-based UPMC, launched Astrata. The company has developed natural language processing technologies that aim to improve the quality of care.

Astrata’s technologies help payers and providers assess care delivery in accordance with quality measures, like the National Committee for Quality Assurance’s Healthcare Effectiveness Data and Information Set (HEDIS). These measures aim to ensure the delivery of high-quality care and mitigate gaps.

Increasingly, the government and private payers are tying provider compensation to quality measures amid the move toward value-based care.

The company’s technologies can comb through hundreds of millions of clinical notes, and by applying natural language processing, can identify whether a patient has received care that is compliant with quality measures, said Dr. Rebecca Jacobson, president of Astrata, in a phone interview.

“[The platform] is really evaluating all the text across one member or one patient and [then it] says, yes this person is in the denominator, that is, the measure applies to them,” she explained.

Once the platform has determined whether a measure applies to a patient, it then assesses whether the care they received is compliant with that measure.

For example, there is a HEDIS measure that focuses on older women with bone fractures who have not yet received appropriate imaging and treatment. Astrata’s platform is able to analyze clinical notes and pinpoint those patients.

“That’s a true gap,” said Jacobson. “That is someone that the health plan and the provider want to know about because they want to intervene quickly so that they can prevent subsequent morbidity and mortality.”

Further, the platform allows healthcare organizations to conduct year-round monitoring and quality improvement efforts as opposed to the current system, where quality rates for many HEDIS measures can only be determined once a year via a manual process.

Astrata partnered with UPMC Health Plan to develop and validate its technologies. The platform has been used by UPMC and UPMC Health plan for several years.

“Over the last two years, UPMC Health Plan abstractors found they can work up to 38 times faster with the implementation of Astrata’s NLP-assisted tools,” said Diane Holder, president and CEO of UPMC Health Plan, in a news release. “This partnership facilitates a more rapid and accurate flow of thorough, meaningful data between our quality team and our providers.”

With the launch of the spinout, Astrata’s tools are now available in the U.S. market.

The company also plans to increase its workforce by 30% in 2021.

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Paige, A computational pathology startup spun out of Memorial Sloan Kettering, raised $100 million in a series C funding round. The New York-based company is developing clinical decision support tools for pathologists, and plans to use the funds to further advance its technology.

Paige was started in 2018 by Dr. Thomas Fuchs, who spun out the company from research at Memorial Sloan Kettering Cancer Center. The startup had a research agreement to receive de-identified images of digitized pathology slides, which it is using to make AI tools across multiple cancer subtypes, and the Memorial Sloan Kettering holds an equity stake in the company.

Earlier this year, Paige received 510(k) clearance from the Food and Drug Administration for a digital pathology image viewer. It also received a Breakthrough Device designation from the FDA for an AI tool for cancer diagnosis.

None of Paige’s clinical decision support tools are yet cleared to be used for diagnostics in the U.S. But in Europe, it has two CE-marked solutions to detect areas of suspected prostate cancer or breast cancer.

With the new funds, Paige plans to double its headcount, with roughly 70 new employees across its engineering and commercial teams.

“This investment reaffirms the vast potential of the Paige platform for clinical and biopharmaceutical drug development applications,” Paige CEO Leo Grady said in a news release. “These funds will enable us to build additional AI-based products within and outside of oncology, deliver these products to laboratories and clinicians globally, and invest in our talent across engineering and commercial functions.”

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BioIntelliSense makes small, adhesive sensors that can monitor a patient’s vital signs. Photo credit: BioIntelliSense

Remote-monitoring startup BioIntelliSense received funding from the U.S. Army Medical Research and Development Command (USAMRDC) to see if its wearable sensor “sticker” could be used to detect Covid-19 symptoms early. The Golden, Colo.-based startup, and Royal Philips (NYSE: PHG), received a $2.8 million award to use wearable to identify Covid-19 cases before symptoms appear.

BioIntelliSense received FDA clearance a year ago for a small, adhesive sensor it developed that can monitor a patient’s heart rate, respiratory rate and skin temperature, as well as their gait, body position or coughing. At the beginning of 2020, it struck a partnership with Colorado-based health system UCHealth to use its device leading up to a surgery or for postoperative care.

Now, the companies plan to enroll 2,500 patients into a study, working with the University of Colorado Anschutz Medical Campus, to validate the startup’s BioSticker device for early symptom detection. People who are experiencing Covid-19 symptoms or who had a recent exposure to the disease are eligible to participate.

If successful, the technology could be used to maximize military preparedness and have a benefit for the general population, Commander Christopher Steele, Director of the USAMRDC’s Military Operational Medicine Research Program, said in a news release.

Philips, which has a minority stake in BioIntelliSense, had been working with the Department of Defense on remote monitoring technologies long before the pandemic. It had specifically been working on a project to use wearables to catch early signs of a bacterial or viral infection before symptoms appear, with the idea of detecting an unknown agent.

Over the summer, Philips rolled out a version of the system that was specifically designed for Covid-19.

“No one organization will be able to combat Covid-19 alone, but working together, we hope to develop a solution that will allow people to understand if they are in the early stages of illness, and take the appropriate actions to help limit spread and get the treatment they need,” Vitor Rocha, Chief Market Leader of Philips North America, said in a news release. “This could help give people confidence in getting back to school, work, travel, or just coming together as a family.”

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Text-based primary care company Curai Health raised $27.5 million in funding.  The Palo Alto-based startup was co-founded in 2017 by Neal Khosla, who previously worked in machine intelligence at Google, and Xavier Amatriain, who built Netflix’s recommendation engine. Curai also recruited MDLive’s former chief medical officer, Dr. Sylvan Waller.

Khosla said he had the idea for the company as he began thinking more about healthcare access. Why does it take most patients two to three weeks to see a physician, and why do so many people go to Google with their healthcare questions instead of a healthcare professional?

“We have this amazing supply of knowledgeable doctors in our country and we need to figure out how to scale them,” he said.

Up to this point, Curai has been consumer-facing, with a relatively low cost of about $8 per visit. Going forward, the company hopes to build out partnerships with employers, payers, and public sector organizations, to make its text-based primary care service available to more employees.

“We see it as not just being a primary care service, but a safety net for healthcare access,” Khosla said. “We can provide you with a primary care physician to help you navigate over time and build a suite of services around that.”

Curai currently offers two different text-based services. The first is somewhat familiar: users can chat with a provider anytime about an urgent health concern. But the startup has also built out a version of text-based care where patients can chat with the same physician across multiple visits, such as if they had questions about a medication or health condition.

Patients’ care teams include a licensed physician in the U.S. and clinical associates, which are trained physicians overseas.

Behind all of this, Curai is building out an AI decision support tool to work with clinicians. For example, it can help with charting, prompting questions for patients before they start a visit, and pulling important information into a patient’s medical record.

The AI system isn’t trained to work with all conditions, but is building up its capabilities over time, Amatriain said.  The company’s algorithms are trained on de-identified healthcare data, and Amatriain added that the company encrypts all patient data to ensure it is secure.

“The AI is humble in a way because it’s learned when it’s being helpful, whether the doctor has accepted (its suggestion) or not,” he said. “The AI learns to be more helpful and suggest things that are more appropriate given the context of the situation.”

Morningside Ventures led Curai’s series B round. Previous investors General Catalyst and Khosla Ventures — the firm founded by Khosla’s father, Vinod Khosla — also participated in the funding round. Morningside’s Stephen Bruso will join the company’s board of directors as part of the deal.

The startup plans to use the funding to expand its platform to additional states. Curai is currently only available in the state of California, but plans to expand to half of the U.S. by next summer.

Photo credit: Venimo, Getty Images

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Money currency vector illustration. Various money bills dollar cash paper bank notes and gold coins. Collection of cash heap pile and currency stack vector set.

It’s been a busy year for Olive AI, the Cleveland-based startup that automates routine administrative tasks for hospitals. Not long after raising $106 million in funding, the company closed a $225.5 million round led by Tiger Global Management, giving it a $1.5 billion valuation.

Since the start of 2020, the company has raised a total of $385 million, with backers including General Catalyst, Drive Capital and GV.

CEO Sean Lane, a former NSA agent, founded the company in 2012, after realizing that most systems in healthcare often aren’t connected. Traveling between hospitals, he noticed a “sea of cubicles” with employees doing the same tasks over and over again.

“In reality, it was like an assembly line,” he said in a previous interview. “Humans were under this mountain of work that they never could get done because of that.”

The solutions handles repetitive tasks in billing, supply chain and pharmacy. For example, it’s often used to manage prior authorizations, vendor contracts and inventory. Some of its customers include MedStar, MemorialCare, Gunderson Health System and Yale New Haven.

Most recently, the company rolled out a solution to surface information to assist with day-to-day tasks — such as surfacing recent patient information, verifying their insurance details and contact information while they’re registering for an appointment.

The company recently hired a new chief financial officer and chief marketing officer to help lead its expansion. It also plans to use the new funds to expand its software capabilities.

“In the year ahead, we’re setting our sights on the big picture — investing in R&D to bring more solutions to hospitals and health systems that not only disrupt the industry, but also help to fix a broken system at a critical time for humanity,” Lane said in a news release.

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Two examples of protein targets in the free modelling category show AlphaFold’s prediction compared to the shape of proteins determined by experimental results. AlphaFold’s predictions are in blue and the experimental results are in green. Screenshot from DeepMind.

DeepMind, the Google subsidiary that has been beating chess and Go players with artificial intelligence, has set its sights on cracking a decades-old problem: predicting the structures of proteins.

At a biennial challenge where participants must blindly predict the structure of 100 proteins based on their amino acid sequences, a system developed by DeepMind captured researchers’ attention when it predicted their shape with a high level of accuracy.

Called AlphaFold, the system determined the shape of around two-thirds of the proteins with an accuracy comparable to time-consuming laboratory experiments. Its accuracy with most of the other proteins was also high, according to results shared by CASP (the Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction) on Monday. The results were compared to the shape of proteins discovered in the lab and were assessed by independent scientists.

This is an important breakthrough because the shape of proteins is closely linked with their function, but it is difficult to predict a protein’s structure based on its amino acid sequence. Proteins can theoretically fold into a multitude of shapes before setting into their final structure. It can take years of research, and expensive equipment, to work out their shape.

“Proteins are extremely complicated molecules, and their precise three-dimensional structure is key to the many roles they perform, for example the insulin that regulates sugar levels in our blood and the antibodies that help us fight infections. Even tiny rearrangements of these vital molecules can have catastrophic effects on our health, so one of the most efficient ways to understand disease and find new treatments is to study the proteins involved,” John Moult, a computational biologist at the University of Maryland in College Park who co-founded CASP, said in a news release.

London-based DeepMind has been working on AlphaFold for four years. It also beat the other teams in the last CASP challenge in 2018, but did so by a much larger margin in the most recent year.

The model’s accuracy is measured using the Global Distance Test, which approximately measures the percentage of amino acid residues within a certain distance from the correct position. In a scale of 1 to 100, DeepMind’s latest AlphaFold system scored a median of 92.4 across all targets.

For the latest iteration of AlphaFold, DeepMind designed a neural network that interprets a protein’s structure as a “spatial graph.” It trained the system on 170,000 protein structures from the protein data bank as well as databases with proteins whose structure was unknown.

This allowed the system to determine structures in a matter of days, the team who developed it wrote in a blog post. An internal confidence measure also indicated which parts of each predicted protein structure are reliable.

What does this all mean? It could have broad implications for drug discovery and better understanding specific diseases. Andrei Lupas, director of the Max Planck Institute for Developmental Biology and a CASP assessor, stated that the system helped his team solve a protein structure that they were stuck on for close to a decade.

Andriy Kryshtafovych, a researcher at UC Davis and one of the judges, described the result as a “triumph for team science,” crediting the collaborative work of researchers over the years to reaching this achievement.

“Being able to investigate the shape of proteins quickly and accurately has the potential to revolutionize life sciences,” he said in a news release. “Now that the problem has been largely solved for single proteins, the way is open for development of new methods for determining the shape of protein complexes – collections of proteins that work together to form much of the machinery of life, and for other applications.”

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