molecule


New MRI sensor can image activity deep within the brain

MIT News

Calcium is a critical signaling molecule for most cells, and it is especially important in neurons. Imaging calcium in brain cells can reveal how neurons communicate with each other; however, current imaging techniques can only penetrate a few millimeters into the brain. MIT researchers have now devised a new way to image calcium activity that is based on magnetic resonance imaging (MRI) and allows them to peer much deeper into the brain. Using this technique, they can track signaling processes inside the neurons of living animals, enabling them to link neural activity with specific behaviors. "This paper describes the first MRI-based detection of intracellular calcium signaling, which is directly analogous to powerful optical approaches used widely in neuroscience but now enables such measurements to be performed in vivo in deep tissue," says Alan Jasanoff, an MIT professor of biological engineering, brain and cognitive sciences, and nuclear science and engineering, and an associate member of MIT's McGovern Institute for Brain Research.


Graduates given the chance to become experts in AI

Daily Mail

Graduates are to be given the chance to become qualified experts in Artificial Intelligence through a new set of masters courses and work-based placements. The move aims to drive up skills in the AI sector with courses and placement being funded by Google's Deepmind and British security and defence giant BAE Systems. This research for Doctoral Training will be made available over five years as part of the scheme. Graduates are to be given the chance to become qualified experts in artificial intelligence (AI) through a new set of masters courses and work-based placements. Advances in modern computing technologies have created an explosion of major breakthroughs in the field of Artificial Intelligence.


New machine learning algorithm can help search new drugs

#artificialintelligence

LONDON, Feb 12: Researchers say they have developed a machine learning algorithm for drug discovery which is twice as efficient as the industry standard, and could accelerate the process of developing new treatments for diseases such as Alzheimer's. The team led by researchers at the University of Cambridge in the UK used the algorithm to identify four new molecules that activate a protein thought to be relevant for symptoms of Alzheimer's disease and schizophrenia. A key problem in drug discovery is predicting whether a molecule will activate a particular physiological process, according to the study published in the journal PNAS. It is possible to build a statistical model by searching for chemical patterns shared among molecules known to activate that process, but the data to build these models is limited because experiments are costly and it is unclear which chemical patterns are statistically significant. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed "Machine learning has made significant progress in areas such as computer vision where data is abundant," said Alpha Lee from Cambridge's Cavendish Laboratory.


AI Is Rapidly Augmenting Healthcare and Longevity

#artificialintelligence

When it comes to the future of healthcare, perhaps the only technology more powerful than CRISPR is artificial intelligence. Over the past five years, healthcare AI startups around the globe raised over $4.3 billion across 576 deals, topping all other industries in AI deal activity. During this same period, the FDA has given 70 AI healthcare tools and devices'fast-tracked approval' because of their ability to save both lives and money. The pace of AI-augmented healthcare innovation is only accelerating. In Part 3 of this blog series on longevity and vitality, I cover the different ways in which AI is augmenting our healthcare system, enabling us to live longer and healthier lives.


AI is reinventing the way we invent

#artificialintelligence

Amgen's drug discovery group is a few blocks beyond that. Until recently, Barzilay, one of the world's leading researchers in artificial intelligence, hadn't given much thought to these nearby buildings full of chemists and biologists. But as AI and machine learning began to perform ever more impressive feats in image recognition and language comprehension, she began to wonder: could it also transform the task of finding new drugs? The problem is that human researchers can explore only a tiny slice of what is possible. It's estimated that there are as many as 1060 potentially drug-like molecules--more than the number of atoms in the solar system. But traversing seemingly unlimited possibilities is what machine learning is good at. Trained on large databases of existing molecules and their properties, the programs can explore all possible related molecules.


Machine Learning Helps Researchers in Hot Pursuit of New Drugs

#artificialintelligence

Researchers have designed a machine learning algorithm for drug discovery which has been shown to be twice as efficient as the industry standard, which could accelerate the process of developing new treatments for disease. The researchers, led by the University of Cambridge, used their algorithm to identify four new molecules that activate a protein which is thought to be relevant for symptoms of Alzheimer's disease and schizophrenia. The results are reported in the journal PNAS. A key problem in drug discovery is predicting whether a molecule will activate a particular physiological process. It's possible to build a statistical model by searching for chemical patterns shared among molecules known to activate that process, but the data to build these models is limited because experiments are costly and it is unclear which chemical patterns are statistically significant.


Machine learning algorithm helps in the search for new drugs

#artificialintelligence

Researchers have designed a machine learning algorithm for drug discovery which has been shown to be twice as efficient as the industry standard, which could accelerate the process of developing new treatments for disease. The researchers, led by the University of Cambridge, used their algorithm to identify four new molecules that activate a protein which is thought to be relevant for symptoms of Alzheimer's disease and schizophrenia. The results are reported in the journal PNAS. A key problem in drug discovery is predicting whether a molecule will activate a particular physiological process. It's possible to build a statistical model by searching for chemical patterns shared among molecules known to activate that process, but the data to build these models is limited because experiments are costly and it is unclear which chemical patterns are statistically significant.


After escaping the Trump chopping block twice, NASA's carbon sleuth will get blasted into space

Mashable

In early 2017, the Trump Administration tried to ax NASA's Orbiting Carbon Observatory 3, or OCO-3. Then, again in 2018, the White House sought to terminate the earth science instrument. Again, the refrigerator-sized space machine persisted. Now, SpaceX is set to launch OCO-3 to the International Space Station in the coming months, as early as April 25. Using a long robotic arm, astronauts will attach OCO-3 to the edge of the space station, allowing the instrument to peer down upon Earth and measure the planet's amassing concentrations of carbon dioxide -- a potent greenhouse gas.


Mol-CycleGAN - a generative model for molecular optimization

arXiv.org Machine Learning

Designing a molecule with desired properties is one of the biggest challenges in drug development, as it requires optimization of chemical compound structures with respect to many complex properties. To augment the compound design process we introduce Mol-CycleGAN - a CycleGAN-based model that generates optimized compounds with high structural similarity to the original ones. Namely, given a molecule our model generates a structurally similar one with an optimized value of the considered property. We evaluate the performance of the model on selected optimization objectives related to structural properties (presence of halogen groups, number of aromatic rings) and to a physicochemical property (penalized logP). In the task of optimization of penalized logP of drug-like molecules our model significantly outperforms previous results.


Penny-Sized Ionocraft Flies With No Moving Parts

IEEE Spectrum Robotics Channel

Insect-scale flying robots are usually designed to mimic biological insects, because biological insects are masters of efficient small-scale flying. These flapping-wing micro air vehicles (FMAVS) approach the size of real insects, and we've seen some impressive demonstrations of bee-sized robots that can take off, hover, and even go for a swim. Making a tiny robot with flapping wings that can move in all of the degrees of freedom necessary to keep it controllable is tricky, though, requiring complicated mechanical transmissions and complicated software as well. It's understandable why the biomimetic approach is the favored one--insects have had a couple hundred million years to work out all the kinks, and the other ways in which we've figured out how to get robots to fly under their own power (namely, propeller-based systems) don't scale down to small sizes very well. But there's another way to fly, and unlike wings or airfoils, it's something that animals haven't managed to come up with: electrohydrodynamic thrust, which requires no moving parts, just electricity.