Proteins are essential building blocks of living organisms. Every human cell is replete with them. While the understanding of the shapes of proteins is important for making medical advances, only a fraction of these had been deciphered until recently. The ability to use artificial intelligence (AI) to predict the structures of almost every protein made by the human body could help to accelerate the discovery of new drugs to treat disease. A program called AlphaFold can predict the structures of 350,000 proteins belonging to humans and other organisms.
Bringing the benefits of artificial intelligence into a company requires good working relationships between the data team and the business units -- and a clear focus on tangible value. Companies embarking on AI and data science initiatives in the current economy should strive for a level of economic return higher than those achieved by many companies in the early days of enterprise AI. Several surveys suggest a low level of returns thus far, in part because many AI systems were never deployed: A 2021 IBM survey, for instance, found that only 21% of 5,501 companies said they had "deployed AI across the business," while the remainder said they are exploring AI, developing proofs of concept, or using pre-built AI applications. Similarly, a VentureBeat analysis suggests that 87% of AI models are never put into production. And a 2019 MIT Sloan Management Review/Boston Consulting Group survey found that 7 out of 10 companies reported no value from their AI investments.
The continued contribution of the drug development community toward improving the quality of lives of patients, researchers, and the public at large, is and will continue to be highly dependent upon the careful execution of strategies to make vast amounts of data meaningful and usable. This is achievable by pairing data with powerful analytics and then using those insights to develop safe and effective processes and products. Although the drug development enterprise is undergoing major transformation, literature about what the sector should do to support and prepare its workforce for these changes is scant. What follows is a discussion of original research conducted by the Tufts Center for the Study of Drug Development (Tufts CSDD) to address workforce development in the era of digitization. The research is primarily based on an in-depth discussion with thought leaders and senior executives. Tufts CSDD identified recurring themes for discussion in articles in academic journals and the trade press between 2015 and 2019. Discussion topics included: (1) challenges and opportunities caused by the sector's digital transformation, (2) skills and competencies of future drug development professionals, (3) new roles that are expected to emerge within drug development, (4) changes in talent recruitment and retention practices, and (5) the reshaping of corporate mindsets and cultures to become digitally proficient organizations.
DeepMind, an artificial intelligence (AI) subsidiary of Google parent Alphabet, said it has been successful in predicting the shape of nearly every protein in the human body as well as thousands of other proteins found in 20 additional organisms that scientists rely on for their research, including yeast, fruit flies, and mice. This breakthrough is likely to assist researchers to understand human diseases better and find new drugs to treat or cure them. Some scientists have compared the DeepMind project to the international effort to map every human gene. DeepMind said in a blog post it is releasing the database for free. To set up and run the database, it has partnered with the European Molecular Biology Laboratory.
Veeva [NYSE: VEEV] is the leader in cloud-based software for the global life sciences industry. Committed to innovation, product excellence, and customer success, our customers range from the world's largest pharmaceutical companies to emerging biotechs. Veeva's software helps our customers bring medicines and therapies to patients faster. We are the first public company to become a Public Benefit Corporation. As a PBC, we are committed to making the industries we serve more productive, and we are committed to creating high-quality employment opportunities.
Quantum computing, AI and blockchain are being explored as drivers for business transformation and intelligent change by leading organizations. Quantum computing has the potential to address the computational needs of modern technological industry development in areas such as drug development and manufacturing, where traditional and supercomputers aren't able to provide the simulations necessary to further enhance and deliver new developments to these industries. Over 60 countries have developed national AI strategies and policies to promote AI development and research and explore risk mitigation using AI. Also, distributed ledger technologies using blockchain are helping to secure data and transactions in areas like finance, government, energy, and transportation. Quantum computing, AI and blockchain naturally coincide, as quantum computing will help bring new levels of computational power and efficiency as data growth and accumulation for industry solutions are on the rise.
Earlier this month, two groups unveiled the culmination of years of work by computer scientists, biologists, and physicists: advanced modeling programs that can predict the precise 3D atomic structures of proteins. Last week, the biggest payoff of that work arrived. One team used its newly minted artificial intelligence (AI) programs to solve the structures of 350,000 proteins from humans and 20 model organisms, such as Escherichia coli bacteria, yeast, and fruit flies, all mainstays of biological research. In the coming months, the group says it plans to expand its efforts to all cataloged proteins—some 100 million molecules. “It's pretty overwhelming,” says John Moult, a protein folding expert at the University of Maryland, Shady Grove, who runs a biennial competition called the Critical Assessment of protein Structure Prediction (CASP). Moult says structural biologists have dreamed for decades that accurate computer models would one day augment slow, painstaking experimental methods, such as x-ray crystallography, that map protein shapes with extreme precision. “I never thought the dream would come true,” Moult says. The computer model, called AlphaFold, is the work of researchers at DeepMind, a U.K. AI company owned by Alphabet, the parent company of Google. In fall of 2020, AlphaFold swept the CASP competition, tallying a median accuracy score of 92.4 out of 100 for its predicted structures, well ahead of the next closest competitor ( Science , 4 December 2020, p. ). But because DeepMind researchers didn't reveal AlphaFold's underlying computer code, other teams were left frustrated, unable to build on the progress. That began to change this month ( Science , 16 July, p. ). On 15 July, researchers led by Minkyung Baek and David Baker at the University of Washington, Seattle, reported online in Science that they had created a competing system: a highly accurate protein structure prediction program called RoseTTAFold, which they released publicly. The same day, Nature rushed out details of AlphaFold in a paper by DeepMind researchers led by Demis Hassabis and John Jumper. Both programs use AI to spot folding patterns in vast databases of solved protein structures. The programs compute the most likely structure of unknown proteins by applying those patterns and also considering basic physical and biological rules governing how neighboring amino acids in a protein interact. In their paper, Baek and Baker used RoseTTAFold to create a structure database of hundreds of G-protein coupled receptors, a class of common drug targets. Now, DeepMind researchers report in Nature that they have amassed 350,000 predicted structures—more than twice as many as experimenters have solved in many decades of work. AlphaFold's structures for which the researchers say they have high confidence cover nearly 44% of all human proteins. AlphaFold determined that many of the remaining human proteins were “disordered,” meaning their shape doesn't adopt a single structure. Such disordered proteins may ultimately adopt a structure when they bind to a protein partner, Baker says. They may also naturally adopt multiple conformations, says David Agard, a structural biologist at the University of California, San Francisco. A database of DeepMind's new protein predictions, assembled with collaborators at the European Molecular Biology Laboratory (EMBL), is freely accessible online. “It's fantastic they have made this available,” Baker says. “It will really increase the pace of research.” Because the 3D structure of a protein largely dictates its function, the DeepMind library is apt to help biologists sort out how thousands of unknown proteins do their jobs. “We at EMBL believe this will be transformative to understanding how life works,” says the lab's director general, Edith Heard. “This will be one of the most important data sets since the mapping of the human genome,” adds Ewan Birney, director of EMBL's European Bioinformatics Institute. DeepMind collaborators say that by making it possible to quickly assess how a change in a protein's sequence alters its structure and function, AlphaFold has already spurred the development of novel enzymes for breaking down plastic waste. It has also prompted efforts to better target parasitic diseases. The impacts aren't likely to stop there. The predictions will help experimentalists who solve structures, Baek says. Data from x-ray crystallography and cryo–electron microscopy experiments can be difficult to interpret, Baek and others say, and having a model can help pinpoint the correct structure. “In the short term, it will boost structure determination efforts,” she predicts. “And over time it will also slowly replace [experimental] structural determination efforts.” If that happens, structural biologists won't find themselves out of work. Baker notes that both experimental and computational scientists are already beginning to turn their efforts to the more complex challenge of understanding exactly which proteins interact with one another and what molecular changes happen during these interactions. The new tools will “reset the field,” Baker says. “It's a very exciting time.” : http://www.sciencemag.org/content/370/6521/1144 : http://www.sciencemag.org/content/373/6552/262
Clinical Trials are the mandatory path for developing and bringing a new drug or vaccine to the market. Unfortunately, according to a study conducted by MIT, 86 percent of the drugs will fail during this process. This very high failure rate not only has consequences on the Pharmaceutical companies' bottom line, but it precludes potentially safe and efficacious drugs from reaching patients that could benefit from them. Recruitment is one of the main bottlenecks, is time-consuming, and very expensive. According to Chunhua Weng from Columbia University (New York), "Recruitment is the number one barrier to clinical research."
Welcome to our July 2021 monthly digest where you can catch up with any AIhub stories you may have missed, get the low-down on recent events, and much more. In this edition we cover ICML 2021, celebrate award winners, check out new AI reports and strategies, and find out who won the AI Song Contest. This month saw the running of the thirty eighth International Conference on Machine Learning (ICML). There were a huge variety of events, including talks, workshops, tutorial, and socials. We were in (virtual) attendance and managed to catch all of the invited talks.