Machine learning model helps characterize compounds for drug discovery

chemical
Credit: CC0 Public Domain

Tandem mass spectrometry is a powerful analytical tool used to characterize complex mixtures in drug discovery and other fields.


Now, Purdue University innovators have created a new method of applying machine learning concepts to the tandem mass spectrometry process to improve the flow of information in the development of new drugs. Their work is published in Chemical Science.

“Mass spectrometry plays an integral role in drug discovery and development,” said Gaurav Chopra, an assistant professor of analytical and physical chemistry in Purdue’s College of Science. “The specific implementation of bootstrapped machine learning with a small amount of positive and negative training data presented here will pave the way for becoming mainstream in day-to-day activities of automating characterization of compounds by chemists.”

Chopra said there are two major problems in the field of machine learning used for chemical sciences. Methods used do not provide chemical understanding

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Nasdaq Women in Technology: Niharika Sharma, Senior Software Engineer, Nasdaq’s Machine Intelligence Lab

Women in Tech: Niharika Sharma

Niharika Sharma is a Senior Software Engineer for Nasdaq’s Machine Intelligence Lab. She designs systems that gather, process and apply machine learning/natural language processing technologies on natural language data, generating valuable insights to support business decisions. Over the past years, she worked on Natural Language Generation (NLG) and Surveillance Automation for Nasdaq Advisory Services. We sat down with Niharika to learn more about how she got her start in computer science and how she approaches challenges in her career.

Can you describe your day-to-day as a senior software engineer at Nasdaq?

My day-to-day work involves collaborating with Data Scientists to solve problems, ideating business possibilities with product teams and working with Data/Software Engineers to transform ideas into solutions.

How did you become involved in the technology industry, and how has technology influenced your role?

My first exposure to Computer Science was a Logo programming class that I took as a

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When human and machine agree about iridium oxide

atoms
Credit: CC0 Public Domain

A human research team and a machine learning algorithm have found that we need to rethink much of what we know about iridium oxide.


Iridium oxide is an excellent catalyst for electrochemical reactions, and is typically used for the production of energy carriers such as hydrogen from water. Now it turns out that research on iridium oxide carried out so far has been based on a wrong basic assumption: The arrangement of the atoms on its surface is completely different to that previously assumed.

The way in which this surprising result was determined gives a tantalizing first glimpse of how research might be performed in the future: a collaborative effort between a human research team and artificial intelligence analyzed the same problem, and came to the same conclusion. Since the researchers at the TU Wien and the TU Munich reached the same result at the same

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Robotic Interviews, Machine Learning And the Future Of Workforce Recruitment

These would affect all aspects of HR functions such as the way HR professionals on-board and hire people, and the way they train them

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Artificial intelligence (AI) is changing all aspects of our lives and that too at a rapid pace. This includes our professional lives, too. Experts expect that in the days ahead, AI would become a greater part of our careers as all companies are moving ahead with adopting such technology. They are using more machines that use AI technology that would affect our daily professional activities. Soon enough, we would see machine learning and deep learning in HR too. It would affect all aspects of HR (human resources) such

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Machine Safeguarding Solutions Market with COVID-19 Recovery Analysis 2020-2024|Growth of End-user Industries to Boost Market Growth

The global machine safeguarding solutions market size is poised to grow by USD 774.41 million during 2020-2024, progressing at a CAGR of almost 4% throughout the forecast period, according to the latest report by Technavio. The report offers an up-to-date analysis regarding the current market scenario, latest trends and drivers, and the overall market environment. The report also provides the market impact and new opportunities created due to the COVID-19 pandemic. Download a Free Sample of REPORT with COVID-19 Crisis and Recovery Analysis.

This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20201007005678/en/

Technavio has announced its latest market research report titled Global Machine Safeguarding Solutions Market 2020-2024 (Graphic: Business Wire)

The machine safeguarding solutions market is driven by the growth of end-users. Several machining operations that are carried out in the automotive and industrial machine manufacturing industry involve bending, boring, grinding, and milling. Manufacturers use transmission systems such

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Machine learning homes in on catalyst interactions to accelerate materials development — ScienceDaily

A machine learning technique rapidly rediscovered rules governing catalysts that took humans years of difficult calculations to reveal — and even explained a deviation. The University of Michigan team that developed the technique believes other researchers will be able to use it to make faster progress in designing materials for a variety of purposes.

“This opens a new door, not just in understanding catalysis, but also potentially for extracting knowledge about superconductors, enzymes, thermoelectrics, and photovoltaics,” said Bryan Goldsmith, an assistant professor of chemical engineering, who co-led the work with Suljo Linic, a professor of chemical engineering.

The key to all of these materials is how their electrons behave. Researchers would like to use machine learning techniques to develop recipes for the material properties that they want. For superconductors, the electrons must move without resistance through the material. Enzymes and catalysts need to broker exchanges of electrons, enabling new medicines

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Machine learning helps scientists hunt for particles, wrangle floppy proteins and speed discovery | US Department of Energy Science News

30-Sep-2020

Daniel Ratner, head of SLAC’s machine learning initiative, explains the lab’s unique opportunities to advance scientific discovery through machine learning.

DOE/SLAC National Accelerator Laboratory

Machine learning is ubiquitous in science and technology these days. It outperforms traditional computational methods in many areas, for instance by vastly speeding up tedious processes and handling huge batches of data. At the Department of Energy’s SLAC National Accelerator Laboratory, machine learning is already opening new avenues to advance the lab’s unique scientific facilities and research.

For example, SLAC scientists have already used machine learning techniques to operate accelerators more efficiently, to speed up the discovery of new materials, and to uncover distortions in space-time caused by astronomical objects up to 10 million times faster than traditional methods.

The term “machine learning” broadly refers to techniques that let computers “learn by example” by inferring their own conclusions from large sets of

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Build a money-generating machine that pays all your bills, become 1st Generation Rich and have fun doing it

Mark J. Migliaccio announces the release of ‘1st Generation Rich’

ALBUQUERQUE, N.M., Sept. 30, 2020 (GLOBE NEWSWIRE) — Mark J. Migliaccio wanted to open the eyes of anyone who always wanted to start a business or invest in real estate but never knew how to get started. He wants to show that there are many more options to the daily grind and “working in a job you dislike with people you would never invite to one of your barbecues.” It is for these reasons that he writes “1st Generation Rich” (published by Archway Publishing), a book about how to build a money-generating machine that pays all your bills, become 1st Generation Rich while having fun doing it.

 

In this guide to building wealth, Migliaccio explores how to build a business that puts one in control of his/her valuable time, master the art of making money by investing in real estate

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Wrapping Machine Market : Rising Trends with Top Countries Data, Technology and Business Outlook 2020 to 2026

The MarketWatch News Department was not involved in the creation of this content.

Aug 28, 2020 (The Expresswire) —
Wrapping Machine Market” is valued at 949.4 million USD in 2020 is expected to reach 1141.7 million USD by the end of 2026, growing at a CAGR of 2.6% during 2021-2026, According to New Research Study. 360 Research Reports provides key analysis on the global market in a report, titled “Wrapping Machine Market by Types (Manual (or Hand) Wrapping Machine, Semi-Automatic Wrapping Machine, Automatic Wrapping Machine), Applications (Food Industry, Chemical Industry, Pharmaceutical Industry, Other) and Region – Global Forecast to 2026” BrowseMarket data Tables and Figures spread through122 Pages and in-depth TOC onWrapping Machine Market.

COVID-19 can affect the global economy in three main ways: by directly affecting production and demand, by creating supply chain and market disruption, and by its financial impact on firms and financial markets.

Final

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AI Machine Learning Breakthrough Is a Twist on Brain Replay

Geralt/Pixabay

Source: Geralt/Pixabay

Recently, researchers affiliated with the Baylor College of Medicine, the University of Cambridge, the University of Massachusetts Amherst, and Rice University created a new way of adapting a neuroscience concept called “brain replay” to the digital realm of artificial neural networks to enable continuous learning.

From a neuroscience perspective, the concept of brain replay is analogous to a streaming service that activates repeat showings from its vast archives of stored pre-recorded content. The brain can replay memories by reactivating the neural activity patterns that represent prior experiences whether asleep or awake. This ability for memory replay starts in the hippocampus, then continues in the cortex.

The research trio of Hava Siegelmann, Andreas Tolias, and Gido van de Ven published a study in Nature Communications on August 13, 2020 that shows state-of-the-art performance from neural networks by deploying a new twist on mimicking brain replay.

From an educational perspective,

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