We believe AI will help solve some of the world's most complex problems - drug discovery / ‘computational biology’ is one of those areas given the vast troves of input data required which make it difficult for humans to process and analyse.
We are therefore delighted to be backing a world-leading team of technical founders who are building MiLaboratories, a leading global AI-driven data intelligence platform in the field of drug discovery and computational biology.
We believe that MiLaboratories, coupled with advancing models and developments in AI, will turbocharge the brightest clinicians in developing breakthrough drug discoveries. This is so critical as any advancements in drug discovery can generate huge commercial as well as social benefits.
Market opportunity
~$247B is spent annually on drug (therapeutic) discovery R&D. On average, it takes 10 years and can cost upwards of ~$2 billion to bring each new drug to market, whilst only ~10% of drug development programs successfully make it to market.
Recent advancements in computing power and AI are creating new opportunities for this market to optimise that R&D spend. A key factor driving that opportunity is data.
The amount of genomics data generated in recent decades has increased from approximately ten megabytes per year in the mid-1980s to over 20 petabytes from 2015–19, an increase of over nine orders of magnitude - for reference, 1 petabyte = 1m gigabytes. The proliferation of data around drug discovery is creating demand for modern software infrastructure and opening up an opportunity for influence by large AI models.
Leveraging decades of academic research and the latest advancements in AI, MiLaboratories’ data intelligence platform has the potential to save years and $10s or even $100s of millions of dollars for each of its customers by materially reducing the time it takes to develop successful therapeutic drug discoveries (such as cancer cures and vaccines).
Problem:
To understand how MiLaboratories fits into this space, it’s worth outlining the steps involved in their customers bringing a drug to market.
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Every year, biopharma companies including the likes of AstraZeneca and Moderna will develop different ideas (’Therapeutic Formulations’ per the above chart) for new treatments e.g. a vaccine for a specific form of cancer. Each of these formulations needs to be validated to see which one might ultimately work and be brought to market.
As part of that validation, companies are required to run a pipeline of processes, ranging from tests in the lab through to human trials, taking blood samples from donors/patients, who are either healthy or ill, at various stages (i.e. pre-vaccine, post-vaccine, etc) before processing the results.
Every stage of this pipeline generates huge volumes (terabytes and sometimes petabytes) of genetic data - which is unstructured.
Firstly, trying to integrate these disparate data sources itself is a big problem given the sheer volume and the reality that the data may be collected from multiple sources, including patients over different timeframes, in different formats. Trying to then process and synthesise this unstructured data into a concise output that scientists can use to make key decisions requires expensive IT infrastructure/hardware, different sets of point solution software tools, and technical staff.
Furthermore, the clunky process involved in synthesising this data, means there are lengthy and costly feedback loops between the multiple stakeholders involved in the drug development pipeline. These stakeholders include the clinicians/biologists managing and running the pipeline, supported by bioinformatic teams (data science teams focused specifically on biological data analysis) and IT teams.
In aggregate, across the pipeline, it can therefore take months and even years of cycling through feedback between these stakeholders across each stage of the pipeline to work out the optimum pathway and generate successful outcomes.
Vision & Solution:
This is where MiLaboratories comes in - their platform, with its products MiXCR (a highly specialised product currently used by data science and bioinformatics teams) and Platforma (which will be used by clinicians/scientists), facilitates the transformation of raw unstructured data into actionable insights, compressing these feedback cycles by orders of magnitude such that, what currently takes months or even years, can be achieved in just a few days.
Enabling customers to fail faster and spend less, optimising investments of time and resources, and increasing the probability of success, MiLaboratories delivers $10s or even $100s of millions of dollars of value for customers.
MiLaboratories' world-class team of PhDs originally built their initial solution while in academia, open-sourcing it to biopharmaceutical researchers globally. Global industry leaders started using their open-source software ~8 years ago. Having since commercialised their offering, generating strong early traction, we believe that MiLaboratories’ data intelligence platform has the potential to become the global standard and force multiplier for the smartest scientists in the multi-hundred billion dollar field of drug discovery.
Why we invested:
Exceptional founding team:
Similar to some of TEN13’s other investments, MiLaboratories’ team had originally designed the algorithms that underpin their current products, in academia. The three technical founders have known each other and worked together for 10+ years.
- CEO - Stanislav Poslavsky - with a PhD in theoretical physics, Stan originally worked at Large Hadron Collider (part of CERN - the European Organisation for Nuclear Research and one of the world's largest and most respected centres for scientific research), before switching to immunology and bioinformatics, when he began to develop the algorithms that underpin MiLaboratories' platform.
- CSO - Dmitriy Chudakov - PhD, DSc in biology. Dmitriy is a world-leading scientist in this space and a leading figure at CEITEC (Central European Institute of Technology).
- CTO - Dmitriy Bolotin - with 17+ years of technology experience in biological software, Dmitriy is the author of 42 research publications, patents, and innovations.
- COO - Alexey Nechaev - began his career in investment Banking at BAML (London), and subsequently moved to work as a VC investor at Picus Capital (US), Alexey then completed his MBA from UC Berkley and joined MiLaboratories as COO.
Leveraging AI to solve a genuine problem in a large and fast-growing market
The pharmaceutical industry operates with a similar power law to venture capital, in that a small percentage of the drugs/vaccines that make it to market only ~10% of drug development programs successfully make it to market) deliver the vast majority of the dollar returns. However, the amount invested in therapeutic R&D, across all drugs/vaccines, whether they make it to market or not, continues to grow, with ~$247B spent on therapeutic R&D per year. On average, it takes 10 years and can cost upwards of ~$2 billion to bring a new drug to market.
- Acting as a picks-and-shovels AI play, MiLaboratories’ platform can take billions of lines/terabytes or petabytes of unstructured genetic data and deliver to their customers, in just a few days or even hours, what usually takes months to gather feedback between scientists and other stakeholders (biologists / IT / Bioinformatics) to generate results.
- With the goal of becoming the number one software for biological data analysis, MiLaboratories is well positioned to capture further value of the predicted $54B in annual savings in R&D that will be enabled through AI-first solutions.
We are delighted to be joining the company’s Series A round alongside European lead VC Kfund, as well as Speedinvest, Acrobator Ventures, Somersault Ventures, EGB Capital and Courtyard Ventures.
Safe Travels!
Seamus and the TEN13 Team