They are large complex molecules, made up of chains of amino acids, and what a protein does largely depends on its unique 3D structure. Figuring out what shapes proteins fold into is known as the "protein folding problem", and has stood as a grand challenge in biology for the past 50 years. In a major scientific advance, the latest version of our AI system AlphaFold has been recognised as a solution to this grand challenge by the organisers of the biennial Critical Assessment of protein Structure Prediction (CASP). This breakthrough demonstrates the impact AI can have on scientific discovery and its potential to dramatically accelerate progress in some of the most fundamental fields that explain and shape our world.
The challenges faced by many businesses, including pharma, have been unprecedented. But the challenges faced have taught us how to do better with less (e.g. less travel, less rep activity etc), and it has hastened innovation and pharma have strived to meet the customer were they are – digitally. This has resulted in some spectacular innovation from both AI and other tech that are ripe to be used with pharma for rapid results to close out 2020 with stronger numbers. In our membership we are constantly seeking out quick and effective (and cost effective in many cases) AI and FutureTech powered solutions to common pharma challenges. Here are some quick wins that you can implement very quickly (or we can do for you) to get results showing up for you before the month is out.
DeepMind, an AI research lab that was bought by Google and is now an independent part of Google's parent company Alphabet, announced a major breakthrough this week that one evolutionary biologist called "a game changer." "This will change medicine," the biologist, Andrei Lupas, told Nature. The breakthrough: DeepMind says its AI system, AlphaFold, has solved the "protein folding problem" -- a grand challenge of biology that has vexed scientists for 50 years. Proteins are the basic machines that get work done in your cells. They start out as strings of amino acids (imagine the beads on a necklace) but they soon fold up into a unique three-dimensional shape (imagine scrunching up the beaded necklace in your hand).
Within any living organism, there are thousands of different proteins, each with its own unique shape. For decades, the exact formation of those shapes has been a pain for scientists to figure out. How exactly does a protein, which starts as a string of amino acids, fold itself into the funky 3D shapes you might recognize from diagrams? AlphaFold, an AI from DeepMind, may have an answer. It can predict, with heretofore unseen accuracy, the shape a protein will take.
In 2020, OpenText surveyed 125 pharmaceutical executives to determine how familiar each respondent is with AI technologies within their industry. The survey results revealed that an interest in AI increased to 85% in 2020, up from 47% in 2018, when a previous similar survey was conducted. Approximately 75% of respondents indicated they intend to or plan on using data scientists' analytics centers of excellence. Unlike with previous OpenText surveys, respondents indicated that issues with regulation and promotional content shy rocked to the top of the least organized areas, as opposed to document management and processing of regulatory submissions, which were well-defined areas. The incorporation of AI into the pharma industry provides a number of tangible advantages. Further findings from the recent OpenText survey revealed, the percentage of companies looking at next-generation technologies dropped from 23% in 2018 to 19% in 2020.
Every so often, a technology with the potential to disrupt clinical practice emerges and the medical literature explodes with new studies. These seismic events present a challenge to the peer review process because many reviewers and editorial board members may be unfamiliar with how to evaluate them. Complicating matters, early adopters and thought leaders may not use consistent terminology, may not report results similarly, or may not appreciate fully the potential for inaccurate conclusions based on interpretation errors.
Artificial Intelligence (AI) has been a top trend in many industries lately, attracting massive media attention and investments. Over the last decade, this complex area of research has rapidly progressed from being a "resurrected cool technology from the past" to a full-blown driver of nothing less than a new industrial revolution -- a digital one. As of today, AI is widely commercialized in such applications as manufacturing robots, smart assistants (e.g. Siri), automated financial investing systems, virtual travel booking agents, social media monitoring tools, conversational bots, surveillance systems, online security systems, language translators, self-driving cars, and much more. In some industries, AI (including its many technologies and sub-disciplines, such as deep learning, recommender systems, and natural language processing), is becoming a standardized component rather than a cutting-edge innovation it once was. This rapid progress in AI adoption is also seen in the pharmaceutical industry -- not without caveats, however. Unlike "mainstream" use cases, like image recognition or spam email filtering, drug discovery research appears to be a much harder case for several reasons.
Artificial Intelligence (AI) and Covid-19 are the two remarkable buzzwords in recent times. AI has seen unprecedented developed by availing technology to fight the Covid-19 crisis. AI has contributed to the virus detection and tracking, and mainly to vaccine production. With 2020 coming to an end, Florian Douetteau, CEO and co-founder of Dataiku talked to Analytics Insight on his predictions of AI policies for 2021. Dataiku provides enterprise AI tools for companies like Pfizer, GE and Unilever.
Artificial intelligence (AI) has solved one of biology's grand challenges: predicting how proteins fold from a chain of amino acids into 3D shapes that carry out life's tasks. This week, organizers of a protein-folding competition announced the achievement by researchers at DeepMind, a U.K.-based AI company. They say the DeepMind method will have far-reaching effects, among them dramatically speeding the creation of new medications. “What the DeepMind team has managed to achieve is fantastic and will change the future of structural biology and protein research,” says Janet Thornton, director emeritus of the European Bioinformatics Institute. “This is a 50-year-old problem,” adds John Moult, a structural biologist at the University of Maryland, Shady Grove, and co-founder of the competition, Critical Assessment of Protein Structure Prediction (CASP). “I never thought I'd see this in my lifetime.” The body uses tens of thousands of different proteins, each a string of dozens to hundreds of amino acids. The order of the amino acids dictates how the myriad pushes and pulls between them give rise to proteins' complex 3D shapes, which, in turn, determine how they function. Knowing those shapes helps researchers devise drugs that can lodge in proteins' crevices. And being able to synthesize proteins with a desired structure could speed development of enzymes to make biofuels and degrade waste plastic. ![Figure] CREDITS: (GRAPH) C. BICKEL/ SCIENCE ; (DATA) CASP For decades, researchers deciphered proteins' structures using experimental techniques such as x-ray crystallography or cryo–electron microscopy (cryo-EM). But such methods can take years and don't always work. Structures have been solved for only about 170,000 of the more than 200 million proteins discovered across life forms. In the 1960s, researchers realized if they could work out all interactions within a protein's sequence, they could predict its shape. But the amino acids in any given sequence could interact in so many different ways that the number of possible structures was astronomical. Computational scientists jumped on the problem, but progress was slow. In 1994, Moult and colleagues launched CASP, which takes place every 2 years. Entrants get amino acid sequences for about 100 proteins whose structures are not known. Some groups compute a structure for each sequence, while others determine it experimentally. The organizers then compare the computational predictions with the lab results and give the predictions a global distance test (GDT) score. Scores above 90 on the 100-point scale are considered on par with experimental methods, Moult says. Even in 1994, predicted structures for small, simple proteins could match experimental results. But for larger, challenging proteins, computations' GDT scores were about 20, “a complete catastrophe,” says Andrei Lupas, a CASP judge and evolutionary biologist at the Max Planck Institute for Developmental Biology. By 2016, competing groups had reached scores of about 40 for the hardest proteins, mostly by drawing insights from known structures of proteins that were closely related to the CASP targets. When DeepMind first competed, in 2018, its algorithm, called AlphaFold, relied on this comparative strategy. But AlphaFold also incorporated a computational approach called deep learning, in which the software is trained on vast data troves—in this case, the sequences and structures of known proteins—and learns to spot patterns. DeepMind won handily, beating the competition by an average of 15% on each structure, and winning GDT scores of up to about 60 for the hardest targets. But the predictions were still too coarse, says John Jumper, who heads AlphaFold's development at DeepMind. “We knew how far we were from biological relevance.” So the team combined deep learning with an “attention algorithm” that mimics the way a person might assemble a jigsaw puzzle: connecting pieces in clumps—in this case clusters of amino acids—and then searching for ways to join the clumps in a larger whole. Working with a computer network built around 128 machine learning processors, they trained the algorithm on all 170,000 or so known protein structures. And it worked. In this year's CASP, AlphaFold achieved a median GDT score of 92.4. For the most challenging proteins, AlphaFold scored a median of 87, 25 points above the next best predictions. It even excelled at solving structures of proteins that sit wedged in cell membranes, which are central to many human diseases but notoriously difficult to solve with x-ray crystallography. Venki Ramakrishnan, a structural biologist at the Medical Research Council Laboratory of Molecular Biology, calls the result “a stunning advance on the protein folding problem.” All groups in this year's competition improved, Moult says. But with AlphaFold, Lupas says, “The game has changed.” The organizers even worried DeepMind may have cheated somehow. So Lupas set a special challenge: a membrane protein from a species of archaea, an ancient group of microbes. For 10 years, his team had tried to get its x-ray crystal structure. “We couldn't solve it.” But AlphaFold had no trouble. It returned a detailed image of a three-part protein with two helical arms in the middle. The model enabled Lupas and his team to make sense of their x-ray data; within half an hour, they had fit their experimental results to AlphaFold's predicted structure. “It's almost perfect,” Lupas says. “They could not possibly have cheated on this. I don't know how they do it.” As a condition of entering CASP, DeepMind—like all groups—agreed to reveal sufficient details about its method for other groups to re-create it. That will be a boon for experimentalists, who will be able to use structure predictions to make sense of opaque x-ray and cryo-EM data. It could also enable drug designers to work out the structure of every protein in new and dangerous pathogens like SARS-CoV-2, a key step in the hunt for molecules to block them, Moult says. Still, AlphaFold doesn't do everything well. In CASP, it faltered on one protein, an amalgam of 52 small repeating segments, which distort each others' positions as they assemble. Jumper says the team now wants to train AlphaFold to solve such structures, as well as those of complexes of proteins that work together to carry out key functions in the cell. Even though one grand challenge has fallen, others will undoubtedly emerge. “This isn't the end of something,” Thornton says. “It's the beginning of many new things.” : pending:yes
A "deep learning" software program from Google-owned lab DeepMind showed great progress in solving one of biology's greatest challenges – understanding protein folding. Protein folding is the process by which a protein takes its shape from a string of building blocks to its final three-dimensional structure, which determines its function. By better predicting how proteins take their structure, or "fold," scientists can more quickly develop drugs that, for example, block the action of crucial viral proteins. Solving what biologists call "the protein-folding problem" is a big deal. Proteins are the workhorses of cells and are present in all living organisms.