Pharmaceuticals & Biotechnology


The Robots are Coming: Is AI the Future of Biotech?

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AI, or artificial intelligence, has taken root in biotech. In this article, we explore its newfound niches in the industry. Artificial intelligence (AI) and machine learning (ML) have become ubiquitous in tech startups, fueled largely by the increasing availability and amount of data and cheaper, more powerful computers. Now, if you are a new tech startup, ML or AI capabilities represent your minimum ticket to enter the industry. Over the past few years, AI and ML have started to peek their heads into the realm of biotech, due to an analogous transformation of biotech data.


AI in healthcare. Does AI provide answer to the problme of drug discovery

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R&D is critical in the world of healthcare, but it is also tricky; progress is slow, and further progress is hampered by the way different researchers and drug developers work in silos, data is kept secret, locked away from the rest of the world. Can AI in healthcare come to the rescue? Vas Narasimhan, CEO at Novartis, recently warned of a problem finding new data. In an interview with Bloomberg, he said that this lack of data has in part caused his initial enthusiasm for AI to turn more cautious. This may yet prove to be the single biggest hurdle in applying AI in healthcare to help find cures to new diseases, extend life, and improve the quality of life.


Want to know when you're going to die?

MIT Technology Review

It's the ultimate unanswerable question we all face: When will I die? If we knew, would we live differently? So far, science has been no more accurate at predicting life span than a $10 fortune teller. But that's starting to change. The measures being developed will never get good enough to forecast an exact date or time of death, but insurance companies are already finding them useful, as are hospitals and palliative care teams.


'Secret sharing': Researchers say they've found way to better encrypt genetic data

The Japan Times

WASHINGTON – Using nothing more than a simple vial of saliva, millions of people have created DNA profiles on genealogy websites. But this wealth of information is effectively inaccessible to genetics researchers, with the sites painstakingly safeguarding their databases, fearful of a leak that could cost them dearly in terms of credibility. This problem of access is one that Bonnie Berger, a professor of mathematics at Massachusetts Institute of Technology, and her colleagues think they can solve, with a new cryptographic system to protect the information. "We're currently at a stalemate in sharing all this genomic data," Berger told AFP. "It's really hard for researchers to get any of their data, so they're not really helping science. "No one can gain access to help them find the link between genetic variations and disease," she said. "But just think what could happen if we could leverage the millions of genomes out there." The idea of this new cryptographic method, described ...


AI for drug development: What's possible and what's just hype? - STAT

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If a group of chemists found 18 more potent versions of a drug out of a sea of 3,000 potential chemicals in the span of a few weeks, they might be hailed as superhumans. That actually happened at Relay Therapeutics, said Dr. Donald Bergstrom, the company's head of R&D. But the driving force behind it wasn't human at all -- it was artificial intelligence. AI and machine learning have been hailed as a powerful new tool for drug discovery. But despite the hype, there is still a huge gap between the potential and the reality.


Cryptographic protocol enables greater collaboration in drug discovery

MIT News

MIT researchers have developed a cryptographic system that could help neural networks identify promising drug candidates in massive pharmacological datasets, while keeping the data private. Secure computation done at such a massive scale could enable broad pooling of sensitive pharmacological data for predictive drug discovery. Datasets of drug-target interactions (DTI), which show whether candidate compounds act on target proteins, are critical in helping researchers develop new medications. Models can be trained to crunch datasets of known DTIs and then, using that information, find novel drug candidates. In recent years, pharmaceutical firms, universities, and other entities have become open to pooling pharmacological data into larger databases that can greatly improve training of these models.


Advanced Imaging and Image Analysis Services: Digital Pathology, Machine Learning and 3D Cell Culture Models

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Three major opportunities for improvement in early-stage in vitro and animal model studies are to improve the predictive capability of in vitro models themselves, the extraction of more complete data from cell cultures and animal models, and to shift from qualitative histological evaluation to a quantitative digital pathology approach. Through this webinar, Visikol will focus its discussion on the use of 3D cell culture models, the application of 3D tissue imaging for studying complex phenomena such as angiogenesis and how digital pathology and machine learning can be used to extract quantitative data from tissues. Several areas of ongoing research being pursued at Visikol will be discussed. Michael Johnson, PhD is a 2017 Forbes 30 Under 30 honoree and the CEO and Co-Founder of Visikol Inc., which is a bio-imaging company that spun out of Rutgers University in 2016 and that Michael founded along with his fellow PhD candidate Thomas Villani and colleague Nick Crider. Michael's research background has focused on a wide range of projects from remote sensing research with NASA to building light sheet microscopes and producing biofuels.


The Transformation of Healthcare with AI and Machine Learning - InformationWeek

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Research in cutting-edge areas like machine learning continues to demonstrate that computers have the potential to predict outcomes and enhance physicians' performance in a wide range of tasks. For example, the U.S. Food and Drug Administration this year approved the first AI diagnostic -- a test for diabetic retinopathy, that produces a result without the need for human intervention. However, this is just the beginning. Healthcare stands poised for a transformation driven by AI and ML, and fueled by an abundance of data sources – electronic health records, genome sequences, mobile devices, embedded sensors, and even billing records. AI and ML solutions are already being used by thousands of companies with the goal of improving the healthcare experience.


SK Telecom opens developer platform for AI speaker

ZDNet

SK Telecom has opened up access for developers to write services for its artificial intelligence (AI) speaker NUGU. NUGU Developers will allow anybody to design services without the need to know how to code, the telecommunications carrier claimed. Services will go through a review process to see whether it will overheat devices, or whether the service contains profanities, before it is rolled out to NUGU users. The telco is hoping its AI platform will wider application beyond the home, and expand into shopping, security, entertainment, health, and education. SK Telecom said it will also publish an SDK for its AI platform at a later date.


Drug Hunters and Centaur Chemists: How AI is fundamentally changing the way Big Pharma discovers drugs

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Pop into the labs of Manchester biotech C4X Discovery and you could be in for a surprise. Instead of scientists perched at their work stations peering down microscopes or poring over data, you might find them walking around, arms outstretched, holding what would appear to be imaginary objects. These scientists haven't gone mad. They're pioneers of a new field in drug discovery that combines science with the sort of virtual reality technology typically associated with gaming. Using a headset and hand controls they're transported into 3D chambers where they can access a database of virtual molecules to pull and twist to see how they fit together.