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Cloud labs: where robots do the research

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As a chemistry PhD student, Dmytro Kolodieznyi was used to running experiments. But in early 2018, his research advisers asked him to take part in one run by robots instead. They wanted Kolodieznyi, who was developing intracellular fluorescent probes at Carnegie Mellon University in Pittsburgh, Pennsylvania, to spend a month attempting to recreate his research at Emerald Cloud Lab (ECL). The biotechnology company in South San Francisco, California, enables scientists to perform wet-laboratory experiments remotely in an automated research environment known as a cloud lab. If the trial went well, it would help pave the way to the wider use of cloud labs at the university.


Cloud labs: where robots do the research

Nature

As a chemistry PhD student, Dmytro Kolodieznyi was used to running experiments. But in early 2018, his research advisers asked him to take part in one run by robots instead. They wanted Kolodieznyi, who was developing intracellular fluorescent probes at Carnegie Mellon University in Pittsburgh, Pennsylvania, to spend a month attempting to recreate his research at Emerald Cloud Lab (ECL). The biotechnology company in South San Francisco, California, enables scientists to perform wet-laboratory experiments remotely in an automated research environment known as a cloud lab. If the trial went well, it would help pave the way to the wider use of cloud labs at the university.


Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade

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From Israeli lab: First AI-designed antibody enters clinical trials

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Aulos Biosciences is now recruiting cancer patients in Australian medical centers for a trial of the world's first antibody drug designed by a computer. The computationally designed antibody, known as AU-007, was planned by the artificial intelligence platform of Israeli biotech company Biolojic Design from Rehovot, in a way that would target a protein in the human body known as interleukin-2 (IL-2). The goal is for the IL-2 pathway to activate the body's immune system and attack the tumors. The clinical trial will be conducted on patients with final stage solid tumors and will last about a year – but the company hopes to present interim results during 2022. The trial has raised great hopes because if it is successful, it will pave the way for the development of a new type of drug using computational biology and "big data."


How artificial intelligence is revolutionising drug design

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Imagine you wanted to design a drug for a new disease, 'Disease X', about which little is known. Imagine then that you have a machine that could use all the available data in the world about Disease X to identify a potential mechanism of disease and use this to predict which molecules within this mechanism could make suitable targets for drugs against the disease. Then, a machine would virtually design a drug targeting these optimal molecules, building it bit by bit and continuously checking with the target's structure to ensure activity at the desired binding site. Once the drug was "built", it could then be synthesised and, following various rounds of in vitro, in vivo, and clinical testing to validate its efficacy, the drug could be used in clinical practice. Although a machine like this does not yet exist, advocates of artificial intelligence (AI) propose that AI has the potential to revolutionise drug design, turning this imaginary scenario -- at least in part -- into a reality.


Increasing access and equity in healthcare through AI - MedCity News

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One of the racial disparities long seen in healthcare lies in minority races returning less frequently for follow-up appointments. AI and remote patient monitoring can be powerful tools to give providers insight into the day-to-day factors impacting a patient's health. Advanced algorithms can process large data sets including clinical and socioeconomic information to give a holistic view of the individual, and AI has the ability to suggest what approaches will work most successfully to not only get patients activated, but keep them engaged. With the ability to collect data from patient devices and more, AI and patient monitoring provide additional data sources to refine the patient experience, including prime times for engagement – such as attending critical follow-up appointments.


Better data for better therapies: The case for building health data platforms

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The past decade has seen an important and, for many patients, a life-changing rise in the number of innovative new drugs reaching the market to treat diseases such as multiple sclerosis, malaria, and subtypes of certain cancers (such as melanoma or leukemia). In the United States, the Food and Drug Administration approved an average of 41 new molecular entities (including biologic license applications) each year from 2011 to 2020--almost double the number in the previous decade. Despite the immense costs of such achievements, 2 2. Asher Mullard, "New drugs cost US $2.6 billion to develop," Nature Reviews Drug Discovery, December 1, 2014. A major barrier is the daunting challenge of understanding the multifactorial nature of many diseases coupled with the vast set of variables in therapy design. Very few diseases, such as cystic fibrosis, are linked to variants in single genes. Drug development therefore tends to rely on a reductionist, hypothesis-driven approach that narrows the focus to individual cell types or pathways. Focused assays often based on partial information or informed by animal models that never perfectly reflect human disease then attempt to identify single molecules that will benefit patients.


Seven technologies to watch in 2022

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From gene editing to protein-structure determination to quantum computing, here are seven technologies that are likely to have an impact on science in the year ahead. Roughly one-tenth of the human genome remained uncharted when genomics researchers Karen Miga at the University of California, Santa Cruz, and Adam Phillippy at the National Human Genome Research Institute in Bethesda, Maryland, launched the Telomere-to-Telomere (T2T) consortium in 2019. Now, that number has dropped to zero. In a preprint published in May last year, the consortium reported the first end-to-end sequence of the human genome, adding nearly 200 million new base pairs to the widely used human consensus genome sequence known as GRCh38, and writing the final chapter of the Human Genome Project1. First released in 2013, GRCh38 has been a valuable tool -- a scaffold on which to map sequencing reads. This is largely because the widely used sequencing technology developed by Illumina, in San Diego, California, produces reads that are accurate, but short.


When scientific information is dangerous

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One big hope about AI as machine learning improves is that we'll be able to use it for drug discovery -- harnessing the pattern-matching power of algorithms to identify promising drug candidates much faster and more cheaply than human scientists could alone. But we may want to tread cautiously: Any system that is powerful and accurate enough to identify drugs that are safe for humans is inherently a system that will also be good at identifying drugs that are incredibly dangerous for humans. They took a machine learning model they'd trained to find non-toxic drugs, and flipped its directive so it would instead try to find toxic compounds. In less than six hours, the system identified tens of thousands of dangerous compounds, including some very similar to VX nerve gas. Their paper hits on three interests of mine, all of which are essential to keep in mind while reading alarming news like this.


These 2021 Biotech Breakthroughs Will Shape the Future of Health and Medicine

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With 2021 behind us, we're going down memory lane to highlight biotech innovations that shaped the year--with impact that will likely reverberate for many years to come. Covid-19 dominated the news, but science didn't stand still. CRISPR spun off variations with breathtaking speed, expanding into a hefty toolbox packed with powerhouse gene editors far more efficient, reliable, and safer than their predecessors. CRISPRoff, for example, hijacks epigenetic processes to reversibly turn genes on and off--all without actually snipping or damaging the gene itself. Prime editing, the nip-tuck of DNA editing that only snips--rather than fully cutting--DNA received an upgrade to precisely edit up to 10,000 DNA letters in a variety of cells.