What is Benchling?

Benchling is a cloud platform for life sciences R&D. Our software provides biotech and pharmaceutical companies with a unified platform for all of their scientific data. But what does that mean? Who actually uses Benchling? What is R&D? Let’s break this down into a few parts to better understand what we actually do at Benchling. […]

Benchling is a cloud platform for life sciences R&D. Our software provides biotech and pharmaceutical companies with a unified platform for all of their scientific data. But what does that mean? Who actually uses Benchling? What is R&D? Let’s break this down into a few parts to better understand what we actually do at Benchling.

Cloud Platform

Benchling is, first and foremost, a software company. Benchling was founded in 2012 at MIT, where our founders saw a clear need for modern technology and tools to enable their scientific research. At the time, there wasn’t much software available that could support biological research. As a result, scientists had to resort to recording their output in spreadsheets and paper lab notebooks. These methods make it difficult to capture data in the first place, refer back to that data later, and share that data across teams. Even basic questions like “where are the test tubes with this sample located in my lab?” are frustratingly difficult to answer, with data scattered across handwritten notes, spreadsheets, and emails.

With Benchling at their disposal, scientists no longer have to spend hours hunting down information across these scattered sources, freeing up their time to focus on what really matters: science and innovation. In a field where so many cutting-edge discoveries are happening — across medicine, agriculture, food, energy, and beyond — they need cutting-edge software to keep up with the pace of research and development. As the pace of scientific advancement continues to accelerate, more multi-dimensional data sets are being created rapidly, and the sheer amount of data being created within life sciences research is orders of magnitude larger than ever before.

While tools like Excel do make data entry easier, manual data entry is error-prone and can result in inconsistencies in data quality. Researchers still lack a unified platform for experiment tracking and data capture, making it difficult for them to get a birds-eye view of all of their progress. Aggregating information across several experiments is nearly impossible, and key decision-making comes at a cost — if a company is deciding which of their drug candidates to spend millions of dollars to put through clinical trials, it’s crucial that their data is accessible and standardized into a single, secure location to inform those decisions.

Benchling is this unified platform for scientific data. We think of this platformed approach in three axes: centralization, standardization, and automation. With centralized data in a single location, standardized data that conforms to a pre-defined data shape, and automated data capture as experiments are run, we can unlock the ability to answer new questions that were un-answerable before. We can even leverage machine learning techniques to automatically identify bottlenecks in a scientific process, generate scientific analyses, and more — the possibilities are endless.

Life Sciences

Benchling started out in academia, building tools such as our CRISPR tool. Today, we work with both academics across a wide variety of institutions, and early-stage and large biopharmaceutical companies across the globe, including Regeneron, Gilead, and Sanofi.

Benchling’s software platform is built for life sciences, or biology-based research. In medicine, the term used to refer to biology-based drugs is biologics. Biologics are powerful medications that are typically more complex than traditional drugs; they’re made up of several components like sugars, proteins, and DNA, or may even be living entities such as cells and tissues. We refer to this as “large molecule” development because they are physically larger and more complex structures than what’s in the standard medications you’re used to. Some examples of biologics that you may have heard of include insulin and Botox, or Regeneron’s antibody therapy for COVID-19.

In contrast, “small molecule”, or chemistry-based research, refers to more traditional drug development. More than 90% of current drugs on the market are small molecules, which have long been the basis for drug development. Most medications people take fall into this category — Tylenol, Advil, and Claritin, to name just a few. Small molecules are, well, small, and they’re easily digestible in the gastrointestinal tract and absorbed into the bloodstream; that’s why you can take them in pill form, whereas large molecule drugs are more complex and have to be taken via injection or infusion.

While the vast majority of current drugs on the market are “small molecules”, biologics-based medications are at the forefront of medical advances today and are quickly rising in popularity and importance, offering treatment options for many medical conditions with no other options, such as cancer, cystic fibrosis, and Alzheimer’s Disease. Biologics are also prevalent in vaccine development — in fact, some of the biggest organizations working on COVID-19 vaccines are rooted in large-molecule development, and they rely on Benchling.

Large molecules are developed through an intricate process with thousands of steps, and they are complex samples that are difficult to model. That’s why a whole different set of tools and data models are needed to support biologics-based research and development. Between small molecule (chemistry-focused) and large molecule (biology-focused) R&D, there’s a growing amount of research that’s blurring the lines between the two. On the left side of this, you need a data model that can understand chemical structure; on the right side, you need a data model that understands DNA and proteins. Modeling this kind of data is just one of the many challenges we face as we aim to support customers across the product lifecycle.

Outside of medicine, Benchling partners with companies and institutions in a wide variety of scientific verticals.

  • Food science. Synthetically produced foods — such as lab-grown meat — are widely hailed for the potential to cut greenhouse gas emissions, spare animal lives, and help solve the global food crisis. Companies like Memphis Meats and Clara Foods are developing technologies to create animal proteins without the animals. With these technologies, we’re no longer limited by natural resources, and capable of producing foods at a high volume and bringing down the cost to help solve world hunger.
  • Biofuels. Many traditional energy and petroleum companies are moving toward biofuels. By engineering bacteria to make certain ingredients that are used in fuels, they can reduce the amount of toxic waste that is produced during the development process, and even lower greenhouse gas emissions from fuel consumption. What’s more is that biofuels are a renewable resource, providing us with a sustainable energy source for many, many years to come.
  • Materials. The textiles industry has long been a source of environmental hazards — manufacturing materials requires immense amounts of water, and the process also creates toxic byproducts — and there hasn’t been much innovation in the industry until recently. Companies like Bolt Threads are engineering biomaterials like synthetic leather from naturally occurring substances such as spider webs and mushrooms, and these engineered materials are both stronger and more sustainable.

The realm of life sciences is vast and quickly expanding, and Benchling is accelerating the pace of research at every step of the way.

R&D

Research (the “R” in R&D) is just one part of the end-to-end scientific process. This is where the exploration happens — a company may start with a target in mind, such as a specific disease, and explore a wide array of potential candidates to cure that disease. In the research phase, scientists use Benchling to plan their experiments, design molecular structures, record observations, and analyze their results. The goal in this stage is to identify promising candidates to investigate further for their target.

After the initial research phase, companies go into development (the “D” in R&D), where their goal is to show that they can realistically produce the candidate in larger quantities. For therapeutics companies, they’ll also do pre-clinical studies in animals to demonstrate that the drug is safe before testing in humans. After R&D is manufacturing — where the company produces the drug candidate for human consumption in larger and larger quantities, as clinical trials proceed.

Today, Benchling is well set up to capture the research and early development phases; as we look to the future, we’re aiming to expand toward manufacturing. From a business standpoint, this unlocks a very large portion of the market — every year, billions of dollars are poured into in life sciences R&D and that’s only increasing, and even more is spent in manufacturing.

Benchling aims to be the backbone of not just R&D, but also every stage downstream. Benchling will be both a platform for science within an organization, and a center for collaboration that connects researchers with contract research organizations, or CROs, to outsource their experiments — in essence, Benchling will enable you to run a biotech company completely from your laptop. Much like how AWS lowered the barrier to entry of starting a software company, our eventual goal is to do the same with life sciences.

How do we get there?

Like we mentioned earlier, Benchling is, first and foremost, a software company. With our grand vision to accelerate life sciences comes a vast array of new and exciting technical challenges. Here is just a sampling of some of the problems we’re tackling in the coming year:

  • Scaling our search system. A customer’s data is a complex object graph of both structured and unstructured data. Having a robust search system that understands this ontology is essential to a scientist’s workflow. As the pace of scientific advancement accelerates, so does the amount of data being generated, and we need to ensure that our search system can support up to billions of data points easily.
  • Building tools for scientific analytics. When answering questions about their data, scientists need rich analysis tools at their disposal. Benchling will provide these tools to conduct out-of-the-box analyses such as linear regressions and curve fitting, and allow scientists to define and execute arbitrary functions against their data for their unique scientific needs.
  • Supporting complex custom data models. In order to support research across several different scientific verticals, we allow customers to design data models that represent the specific science that they’re doing. In fact, scientists have similar needs as engineers when designing their data models, and we plan to extend our system with features like subclassing, stricter typing, and data versioning.
  • Growing a developer ecosystem. Scientists often leverage lab robots to conduct high-throughput experiments. To automate data capture as these experiments are run, our developer platform provides an interface for data ingestion of the experimental results captured by these lab robots. High-throughput experimentation is becoming more commonplace, and our developer platform will expand to support these complex scientific workflows as we build out a Benchling developer ecosystem.
  • Leveraging Machine Learning. Benchling is the system of record for scientific data. We plan to leverage ML technologies using this data to improve research productivity, streamline operational processes, generate scientific insights, and supercharge the user experience on Benchling.

As we expand into new scientific verticals and research modalities, capture new parts of the market, and continue to build toward our vision of running a biotech company on Benchling, we’ll tackle these and many more challenges along the way.

So…what does “Benchling” mean?

So, we’ve covered what Benchling does and where we’re going. But where does the name come from? And what does the logo represent?

Our logo is a jellyfish with DNA in its tentacles, which is an ode to a basic biology experiment that extracts GFP, or green fluorescence protein, from glow-in-the-dark jellyfish, and inserts it into another cell’s DNA to act as a fluorescent tag. It’s a common experiment that’s conducted in introductory molecular biology classes and used in real-world scientific cases.

The name “Benchling” refers to a scientist’s lab bench — their primary place of work — where Benchling is a scientist’s helper that’s always by their side. Benchling is more than just a tool used every once in a while; it’s a place where scientists work and collaborate day in and day out, as we sit on the forefront of scientific progress.

We’re hiring!

And yes, Benchling is hiring engineers to join our team! If you’re interested in building high-quality software to power a new generation of scientific research, visit our careers page or contact us.


What is Benchling? was originally published in Benchling Engineering on Medium, where people are continuing the conversation by highlighting and responding to this story.

Source: Benchling