Hybrid Similarity Search (HSS) Algorithm for Chemistry Searching for Fentanyl-related compounds and other drugs.
Free version: https://www.mswil.com/images/NIST/NIST17/GCMS-Hybrid-Search-AnalChem-2017.pdf
This is a news from NIST back in March (https://www.nist.gov/news-events/news/2018/03/free-software-can-help-spot-new-forms-fentanyl-and-other-illegal-drugs ) and found with the NIST RSS channel of the Chemical Substances Miner http://www.minerazzi.com/chemsubstances/spp.php
It is a nice example of Information Retrieval applied to Chemistry. They used a modified cosine similarity function. I see possible applications to topic analysis.
Anal. Chem., 2017, 89 (24), pp 13261–13268 DOI: 10.1021/acs.analchem.7b03320
“A mass spectral library search algorithm that identifies compounds that differ from library compounds by a single “inert” structural component is described. This algorithm, the Hybrid Similarity Search, generates a similarity score based on matching both fragment ions and neutral losses. It employs the parameter DeltaMass, defined as the mass difference between query and library compounds, to shift neutral loss peaks in the library spectrum to match corresponding neutral loss peaks in the query spectrum. When the spectra being compared differ by a single structural feature, these matching neutral loss peaks should contain that structural feature. This method extends the scope of the library to include spectra of “nearest-neighbor” compounds that differ from library compounds by a single chemical moiety. Additionally, determination of the structural origin of the shifted peaks can aid in the determination of the chemical structure and fragmentation mechanism of the query compound. A variety of examples are presented, including the identification of designer drugs and chemical derivatives not present in the library.”
This post is part of a series on Mind Retrieval that we started long time ago back in 2010. Links to previous posts can be found here:
The Internet can be thought of as a network of nodes where each node has an assigned IP address. The Web and Deep Web are subsets of this. Traversing these nodes is done with crawlers. To retrieve the information that flows across them is what we know as information retrieval. The information might consists of data, text, sound, images. The text might convey numbers, words, phrases, topics, ideas, theses…
Now if the nodes are human brains, the information extraction from the part of the brain responsible of thoughts, emotions, senses, can be loosely termed Mind Retrieval.
So Mind Retrieval requires communications between nodes (brains). The BrainNet Project represents one step closer toward this direction. Here is an interesting link (http://info.247apk.com/brainnet-can-have-three-brains-talk-to-each-other/) where three human brains were “connected” to accomplish specific tasks.
Imagine a large scale project where the brightest minds can interact to take on bigger tasks or solve important scientific and practical problems. Imagine artificial brains (bot brains) doing the same.
When I started this series years ago, many did not believe in the idea of mind retrieval, teasing it as mere speculations.
I guess they have been retracted since then. Mind Retrieval and brain-to-brain social networks are at a corner near you, along with peripherals: browsers, reprogramming, adverts, teleports, hacks, etc.
Quantum Computing is a new miner, available now at
Find resources relevant to quantum computing, searches, retrieval, and information assurance.
Access from introductory to advanced research papers and how-to articles. This 2017, move beyond classic IR and computing stuff and forward to new research paradigms like quantum information retrieval, quantum searches, quantputers, and their implications to encryption and information security.
During the last 20 years, quantum computing has mature and is now in the fast lane.
We already have quantum computers, quantum programming languages, and quantum pagerank algorithms. We even have quantum hackers and crackers.
So university computer science departments may want to start embracing quantum-oriented research projects and affine technologies. Same goes for private companies and marketing research companies.
So the challenge for this 2017 and upcoming years is…
“To bit, or not to bit, that is the qubit:”
That Quantum Computing and Searching is the next Information Security (IS) and Information Retrieval (IR) frontier is more than clear. According to Phys.org and quote
“The National Institute of Standards and Technology (NIST) is officially asking the public for help heading off a looming threat to information security: quantum computers, which could potentially break the encryption codes used to protect privacy in digital systems. NIST is requesting methods and strategies from the world’s cryptographers, with the deadline less than a year away.”
Read more at:
Now that Quantum Computers and Quantum Searches are at a corner near you, the implications are many: from search marketing to search apps, from social grids, to quantum PCs, from big challenges to big data, from quantum retrieval to mind retrieval: The sky is the limit. Back in 2013 we mentioned quantum searches in the context of XOR/XNOR searches.
A miner on quantum searches will soon be available at http://www.minerazzi.com. In the meantime, see some useful links below:
- Phys.org (2016). NIST asks public to help future-proof electronic information.
- Viamontes, G. F., Markov, I. L., & Hayes, P. (2005). Is Quantum Search Practical?
- Phys.org (2005). Data structures influence speed of quantum search in unexpected ways.
- Quora (2014). How do you use the Grover quantum search algorithm to find all the solutions to some search query?
- Paparo, G. D. & Martin-Delgado, M. A. (2012). Google in a Quantum Network.
- Wang, H., Wu, J., Yang, X., Chen, P., & Yi, X. (2014). An Enhanced Quantum PageRank Algorithm Integrated with Quantum Search.
- Lu, S., Zhang, Y., & Liu, F. (2013). An efficient quantum search engine on unsorted database.
- MIT Technology Review (2011). Quantum PageRank Algorithm Outperforms Classical Version.
This is a new miner, built with our in-context crawler, available now at
12-23-2016 Update: BREAKING NEWS: LIGO discovery named Science’s 2016 Breakthrough of the Year.
As its name states, our LIGO miner finds resources relevant to the Laser Interferometer Gravitational-Wave Observatory (LIGO) project, one of the largest NSF-funded projects and that is praised by the scientific community for its discoveries, receiving a lot of attention, recognition, and prizes all over the world.
The LIGO Project allows scientists to better understand and see the Universe: i.e., to see and analyze gravitational waves due to distant objects and events, like the collision and merging of black holes. It has already proven Albert Einstein theory of gravitational waves. See these links
A course on the subject has been available online for a long time at
To learn more about LIGO, visit these links:
This is a new way of seeing the Universe. It also opens the door for new technologies at the intersection of many disciplines like noise reduction, optics, among others; hence the importance of developing this miner.
Back in 1991, the New York Times reported that experts clashed over the project. Back then Dr. J. Anthony Tyson, an astrophysicist at A.T.&T. Bell Laboratories, and at the time chairman of the Astronomy Advisory Committee of the National Science Foundation, polled astronomers as to their views about the project: “I perused a list of about 2,000 astronomers and picked 70 who seemed to me likely to have thought about LIGO,” Dr. Tyson said in an interview. “I got 60 replies, and they ran 4 to 1 against LIGO. Most of the astrophysical community seems to feel it would be very difficult to get any important information from a gravity-wave signal, even if one should be detected.”
See the full story at this link:
Those detractors were all wrong! 26 years latter, I wonder what happened with them or what they are thinking these days.
The Challenge Now:
Are you ready for LIGO and a new kind of Astronomy?
Would you like to build curated collections about LIGO?
Then try our miner.
Here is a nice Mashable article on the subject
discovered with the miner for the query [einstein].
We have restored, expanded, and updated our tutorial on the BM25 Extension to Multiple Weighted Fields Model, best known as BM25F. It is now available at
Active links were also added to the References section.
IBM researchers have created artificial neurons and synapses.
Very relevant to mind retrieval, in many ways.
Local Term Weight Models from Power Transformations
Development of BM25IR: A Best Match Model based on Inverse Regression
In this article we show how power transformations can be used as a common framework for the derivation of local term weights. We found that under some parametric conditions, BM25 and inverse regression produce equivalent results. As a special case of inverse regression, we show that the largest increment in term weight occurs when a term is mentioned for the second time. A model based on inverse regression (BM25IR) is presented. Simulations suggest that BM25IR works fairly well for different BM25 parametric conditions and document lengths.
Retail Banking is a new Minerazzi miner, available at
Find products, services, and companies relevant to card or cardless ATM software, digital wallets, mobile payments, payment service providers, and more with this new miner. Search by technologies or keywords.
Recrawl a search result to find additional resources or build your own curated collection.
For additional topic-specific miners, visit http://www.minerazzi.com