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
Strictly speaking, humans cannot digest complex carbohydrates — that’s the job of bacteria in our large intestines. UC Riverside scientists have just discovered a new group of viruses that attack these bacteria.
The viruses, and the way they evade counterattack by their bacterial hosts, are described in a new Cell Reports paper.
Bacterioides can constitute up to 60% of all the bacteria living in a human’s large intestine, and they’re an important way that people get energy. Without them, we’d have a hard time digesting bread, beans, vegetables, or other favorite foods. Given their significance, it is surprising that scientists know so little about viruses that prey on Bacteroides.
“This is largely unexplored territory,” said microbiologist Patrick Degnan, an assistant professor of microbiology and plant pathology, who led the research.
To find a virus that attacks Bacteroides, Degnan and his team analyzed a collection of bacterial genomes, where viruses can