LABORATORY INFORMATICS
identification, tagging and extraction), a named entity recognition (NER) and extraction API, which scans document text in real time, at about two million words a second. ‘It can find a word, let’s say Viagra, and
it knows that Viagra is a drug. By looking at word usage and proximity of words in that document, the system can then figure out what Viagra is used for, and then apply that knowledge to find and extract information on Viagra from millions of other documents.’ TERMite has been developed as a
system which pharma companies can ‘plug-into’ their existing analytical software, Harland explains. ‘Just as you find a spellchecker inside
a word processing software, our TERMite application can sit inside scientific data applications, making them instantly more intelligent.’ SciBite has also generated more than 100 ontologies containing many millions of synonyms across topics including genes, drugs, diseases, adverse events,
www.scientific-computing.com | @scwmagazine
”Once you can train a machine to recognise what a word ‘is’, then those words start to have meaning”
all of which are delivered through TERMite. The firm’s TExpress software, which also works with TERMite-processed data, goes a step further and is able to find and extract semantic patterns of biomedical notation within sentences, such as text that describes how a specific gene defect leads to a certain disease. ‘Over the next few years, as we work to
improve the quality of our data even further, AI will be able to ask more sophisticated questions, such as “why” someone is looking at changes to features in cells. When we get to this point, AI will be able to add even further depth of insight, because it understands the “why” of that question. In the cell recognition example, this might
be because we are looking for compounds that can treat cells by generating the changes we are looking for. And then we can start to use AI to look for similarities in the biology of how different compounds work.’
Costs and serendipity in drug discovery The drug discovery process represents a huge financial and resource drain on the industry, and has historically endured a high candidate attrition rate, comments Andrew Hopkins, CEO of UK-based Exscientia. ‘Traditional drug discovery operations account for about 35 per cent of the total cost of bringing a drug to market, and you may have to run 20 drug discovery projects, each one costing $15-20 million, even in the early, preclinical stage, just to get one molecule that will ultimately stand a chance of FDA approval.’ There has always been a large element
of serendipity in the early stages of drug discovery to find promising ‘hits’, he notes, but the experience and insight of the scientist driving each project shouldn’t be
December 2018/January 2019 Scientific Computing World 11 g
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28
