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Science, at its very core, is collaborative.

The essence of scientific work is building upon other science – think of all the research and discovery that would never have occurred otherwise.

Still, scientists, just like other professionals, can work in silos. Their jobs are complex, circuitous, time-consuming, often messy, even more often repetitive. And while the value of AI and machine learning (ML) are by now undisputed, the accumulated data is more than can ever possibly be humanly analyzed.

Code Ocean strives to help scientists get back to the basics – for themselves and humanity. The company developed what it calls the first-of-its-kind, computational-research laboratory SaaS platform for scientific collaboration and discovery. The technology enables scientists across disciplines to standardize workflows and track and reproduce computations and discoveries.


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“The preface of this project was collaboration,” said Simon Adar, CEO and cofounder of the Cornell Tech-incubated company, which today announced a $16.5 million series B funding round.

Image courtesy of Code Ocean.

Accelerating biomarker discovery

The “essential triplet” of any computational research work is code, data and results. According to Adar, Code Ocean’s core is its trademarked Compute Capsule, a container technology that encapsulates reproducible, archival and executable versions of experiments, thus combining that essential triplet.

The easy-to-use automated lab allows researchers to reproduce, reuse and share computational experiments. The capsule also helps to ensure that experiments can be preserved and reused in current and future research, Adar explained.

“It democratizes computational science, providing researchers the freedom to explore various types of scientific questions,” he said.

In the case of the Princess Margaret Cancer Centre in Toronto, that enduring question has been around biomarkers.

These biological molecules are critical in drug discovery, explained Princess Margaret senior scientist Benjamin Haibe-Kains. But there are many ways to suppress a gene, so the delicate balance is finding chemical ingredients that kill cancer without negatively impacting normal cells. And that in the greatest number of people possible. 

Because cancer and the human body are both extremely complex, it is rare that one chemical will help treat cancer in everyone. For instance, a certain chemical may work in just 10% of a population. Researchers are constantly analyzing this to find the best home treatments, Haibe-Kains explained.

“There’s a lot of heterogeneity,” said Haibe-Kains, who is also associate professor in the medical biophysics department at the University of Toronto. “Each cancer is very much unique.”

Increasingly, researchers are using AI, algorithms and deep neural networks to help make connections. But akin to enterprise where cloud computing has become a commodity – and processes such as DNA sequencing have become infinitesimally faster – have meant pileups in unanalyzed data.

With a platform like Code Ocean’s, Haibe-Kains pointed out, scientists can now be more focused on that pursuit, and academia and industry can work symbiotically to develop and share research and methodologies. 

A parallel challenge in industry and academia, he said, is the struggle to get projects to run within departments or even single labs, as scientists need a platform to be able to reuse and build upon each other’s research rather than having to constantly recreate it with every iteration. 

“Every piece of code that has value should be transferable,” Haibe-Kains said. “You can liberate each other’s work.”

Princess Margaret was one of the first users of the Code Ocean platform, and Haibe-Kains described it as “field agnostic.” “It addresses a fundamental need for any researcher, whatever the topic is.”

He added that, “the hope is that not only every paper will be reproducible, but it will increase the way people interact internally even before the paper is published. You can embrace reproducibility even before publication.”

Adar agreed, saying that a consistent, automated virtual lab is “absolutely critical” to today’s science. The Runway postdoc awardee at the Jacobs Technion-Cornell Institute began working on Code Ocean while at Cornell, and previously collaborated with the DLR – the German Space Agency on the European FP7 funded EO-MINERS project to detect environmental changes from airborne and spaceborne sensors.

“Computing has given us vastly expanded speed and, simultaneously, dramatically increased complexity,” he said. “To advance computational science at a faster pace, access to high performance computing for scientists must be simplified to maximize secure, seamless collaboration and speed advances to market.”

Efficient collaboration

Code Ocean launched its first project in 2017 and released its enterprise version two years ago. Its platform is used across biology, biopharma, chemistry, genomics and emerging areas of computational science, such as AI-driven drug R&D. The company counts among its customers Sema4, Champions Oncology, CytoReason and Dragonfly Therapeutics, as well as startups.

The platform is built on the concept of open science, allowing for easy migration of code and data across platforms, and it automates the function of Docker, git repositories, Nextflow and other open-source technologies, Adar explained. It is language agnostic and incorporates cloud workstations for RStudio, Jupyter, Terminal, MATLAB, Shiny, Git and others. It is available in both an enterprise version and as a free public platform for scientific developers and authors. 

The funding round is co-led by Battery Ventures and M12, Microsoft’s Venture Fund, and will be leveraged to build Code Ocean’s team, Adar said. 

The funding announcement comes on the heels of a significant partnership with Lantern Pharma. The Code Ocean platform will power AI-driven computational research for oncology-focused drug discovery at the clinical-stage biopharmaceutical company. As Lantern Pharma president and CEO Panna Sharma explained, the company’s scientists, researchers and data engineers needed a highly secure, collaborative environment to create reproducible science. “Code Ocean’s digital lab has been an ideal environment for our scientists and engineers to design and scale our proprietary AI for drug discovery,” Sharma said, “since it allows them to develop, document and share ideas faster and more efficiently.”

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