A Life-Sciences Algorithm to Accelerate Drug Discovery

Laziness and frustration may seem an unlikely source of inspiration, but it worked for Karin Lachmi. As a biology postdoc at Stanford, Lachmi spent countless hours planning experiments — painstakingly comparing reagents, making purchases, and scouring scientific papers to find the best protocol.

“It’s almost absurd that here we are at one of the most prestigious universities, Stanford, and … I don’t have the Internet tools that we are very used to in other domains,” Lachmi told GEN.

She was using services like OpenTable to find restaurants and Expedia to book flights, but realized that nothing similar existed for biologists. And reading individual papers seemed like a Herculean task, given the ever-increasing number of scientific publications, estimated to double every nine years.

“I’m a lazy researcher,” said Lachmi. “I want all this data to be compiled for me, organized, and structured in a way that will really help me learn from other people that did those experiments before.”

So Lachmi teamed up with entrepreneur Daniel Levitt in 2013 to found Bioz, a company that helps scientists make informed purchasing decisions when planning for an experiment. Bioz’s search engine algorithm combines machine learning, artificial intelligence, and natural language processing to extract information from the methods sections of scientific papers and help researchers pick what product to buy and how to use that product.

After spending three years developing its algorithm, Bioz launched its site in 2016. Since then, the company has attracted 1 million users from 196 countries, and lists information for 200 million products from 50,000 vendors.

Users who are planning a new experiment or optimizing an old one can search for a particular product by name. They can then see all the versions of that product sold by various vendors, each with an Amazon-like star rating, called “Bioz Stars.” Unlike Amazon, ratings are not…

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