The London-based startup LabGenius announced that they raised over $10 million in Series A Funding. They are a drug discovery company that utilizes artificial intelligence (AI), robotic automation, and synthetic biology. Their main focus is to find novel protein therapeutics.
According to Dr James Field, CEO and Founder of LabGenius, “Protein therapeutics have an unparalleled potential to both treat disease and alleviate human suffering. By transforming how these drugs are discovered, we have a shot at improving the lives of countless people. Being able to robustly engineer novel therapeutic proteins has immense commercial and societal value. The discovery of protein therapeutics has historically been highly artisanal, relying heavily on humans for both experimental design and execution. This dependence has proved limiting because, as a species, we’re cognitively incapable of fully grasping the complexity of biological systems.”
The Series A investment round is led by Lux Capital and Obvious Ventures. Other participants included Felicis Ventures, Inovia Capital, Air Street Capital, and other existing investors. CEO and founder of Recursion Pharmaceuticals, Chris Gibson, along with Inovia Capital General Partner Patrick Pichette, are also investing. Pichette is the former CFO of Google.
According to the company, they will use the capital to “scale their team, expand the scope of its discovery platform, and initiate an internal asset development program.” One of their main goals is to evolve novel antibody fragments. These could be used to treat certain conditions that can’t rely on conventional antibody formats.
Lux Capital and Obvious Ventures
Zavain Dar, Partner at Lux Capital, along with Nan Li, Managing Director at Obvious Ventures, have been involved in the life science startup environment for some time. Their investment strategy dates back nine years, including a 2013 investment into Zymergen, a molecule discovery and manufacture company based out of California. In 2016, they were involved in Recursion Pharmaceuticals, who went on to a series C raise of $156 million in July.
Their strategy follows a path, starting at industrial biotech technology with Zymergen and followed by root-cause biology discovery with Recursion Pharmaceuticals. It is closed out by creating composition of matter and IP with LabGenius.
Dar explained his reasoning behind choosing LabGenius over other startups.
“We scoured the globe, and didn’t want to be constrained by what happened to be in our backyard,” he says. “They are leading the pack…and now with backing and pharma partnerships, should be in a good position.”
Humans No Longer Sole Agents of Innovation
When speaking to TechCrunch, Dr James Field said, “My central thesis, the thing that’s really the driving force behind the company, is the conviction that we’re entering an age in which humans will no longer be the sole agents of innovation. Instead, new knowledge, technologies and sophisticated real-world products will be invented by smart robotic platforms called empirical computation engines. An empirical computation engine is an artificial system capable of recursively and intelligently searching a solution space.”
The company has created a discovery platform called EVA, and it integrates multiple new technologies coming from different fields including artificial intelligence. After discovery and characterisation, LabGenius then sends their proprietary molecules to clinics.
Field explains the company’s EVA technology as a “machine learning-driven, robotic platform”,” that is capable of “designing, conducting and critically learning from its own experiments.”
“For decades, scientists, engineers and technologists have dreamt of building ‘robot scientists’ capable of autonomously discovering new knowledge, technologies and sophisticated real-world products,” says Field.
“For protein engineers, that dream has now entered the realm of possibility. The rapid pace of technological development across the fields of synthetic biology, robotic automation and ML has given us access to all the essential ingredients required to create a smart robotic platform capable of intelligently discovering novel therapeutic proteins.”
How Governments Have Used AI to Fight COVID-19
Governments all around the globe are using artificial intelligence (AI) to help fight against the ongoing COVID-19 pandemic. The technology is being used for various different things, including speeding up the development of testing kits and treatments, giving citizens access to real-time data, and tracking the spread of the virus.
Here are some of the different countries.
South Korea’s government, one that is being touted as an example for how to combat the virus, pushed their private sector to start developing testing kits right away, immediately after the reports began to arrive out of China.
One of those companies was Seoul-based molecular biotech company Seegene, which used AI to help quicken the process of developing testing kits. The company was able to submit its solution to the Korea Centers for Disease Control and Prevention (KCDC) just three weeks after the scientists began their work. According to Chun Jong-Yoon, founder and chief executive of the company, the process would have taken at least two to three months without the use of AI.
Since the testing kits are such a crucial part of getting the virus under control, this proves that AI technology can play a huge role in the fight.
Traditionally, the approval process for new medical equipment, including testing kits, takes around 18 months. The KCDC decided to move the process ahead and approved the tests in one week. The government’s own patient samples were able to be used for evaluation.
Telecoms firm KT has also partnered with South Korea government ministries in order to use AI-based healthcare services to track the spread of the virus.
The research project was led by the ICT Ministry and Ministry of Interior and Safety, along with universities and research institutes. KT will be responsible for providing mobile data, who can help create maps. These maps can then provide insight into how the populations are moving and the virus spreading.
Scientists in China used AI in order to speed up scientific processes. Through the use of the technology, they were able to recreate the genome sequence of the virus in a month. Comparing that to the months it took scientists to create the sequence of the SARS virus in 2003, it is a big step up.
In Taiwan, the technology has been used as well. Audrey Tang, Taiwan’s digital minister, relied on AI to develop real-time digital updates. These updates could alert citizens of certain hazardous locations, where infections had been previously detected. They were also able to use it to create a live map of local face mask supplies.
In the United States, the White House Office of Science and Technology Policy called for the development of the COVID-19 Open Research Dataset (CORD-19). This dataset is a collection of thousands of different machine-readable COVID-19 literature. There are over 44,000 scholarly articles that can be used by the research community.
“It’s all-hands on deck as we face the COVID-19 pandemic,” Microsoft’s chief scientific officer, Dr. Eric Horvitz said. “We need to come together as companies, governments, and scientists and work to bring our best technologies to bear across biomedicine, epidemiology, AI, and other sciences. The COVID-19 literature resource and challenge will stimulate efforts that can accelerate the path to solutions on COVID-19.”
While there is still a lot more that can be accomplished with artificial intelligence (AI), these are some of the current examples from around the globe. If governments are convinced by the results, the use of AI during a pandemic could become one the first defense options in the future.
Anton Dolgikh, Head of AI, Healthcare and Life Sciences at DataArt – Interview Series
Anton Dolgikh leads AI and ML-oriented projects in the Healthcare and Life Sciences practice at DataArt and runs educational and training developers focused on solving business problems with ML methods. Prior to working at DataArt, Dolgikh worked in the Department of Complex Systems at the Université Libre de Bruxelles, a leading Belgian private research university.
What was it that originally inspired you to pursue AI and life sciences as a career?
A passion for searching the links between phenomena and facts. I always like to read. I love books. At university, I discovered a new source of information – articles. At some point, it appeared that to get a complete picture, to crystallize the beautiful truth from a mass of information is almost impossible. And here comes AI. Statistics, machine learning of course, and natural science with AI at the top all act to build the bridge between the human brain’s thirst for knowledge and a world where all the laws are known and there are no black boxes.
You currently educate and train developers who are focused on solving business problems with ML methods. Is there a specific field of machine learning that you focus on more, for example deep learning?
Yes, deep learning is a very popular and, let’s be honest, powerful instrument; we cannot neglect it. I personally prefer the Bayesian interpretation of classical algorithms, or even a combination of neural networks and a Bayesian approach — for example, a Bayesian Variational Autoencoder. But I believe that the most important thing to teach new ML guys is not to blindly use ML machinery like a magic black box, but rather perceive the basic principles behind each and every method. A must-have skill is the ability to explain the predictions obtained for a business audience.
In March 2019, you wrote an article called ‘Are we Ready for Machine Radiologists, and their Mistakes?‘. In the article you outlined the pros and cons of accepting results from machine radiologists versus human radiologists. If you had to choose between a human and a machine giving you results, which would you choose and why?
I prefer a human radiologist. Not because I have some special knowledge that AI tis heavily prone to errors and decisions are intrinsically erroneous. No, it’s more a question of empathy and the psychological nature. I want to support human doctors during this difficult period. Moreover, I believe that in the nearest future, we will only see AI augment human ability.
You recently wrote a white paper called ‘The Impact of Artificial Intelligence on Lifespan.’ In this paper, you stated that AI should be viewed as a tool in the search for longer life. What are some of the more promising methodologies that AI could apply to the quest to extend human lifespan?
Today the new tool of AI is beginning to operate in scientific laboratories on par with classical instruments and approaches. This fact itself is promising. AI is here to help, not replace, us in the struggle to cope with the huge quantities of data flooding not only laboratories but even our personal lives.
Also discussed in the same white paper is a claim by Biogerontology Research Foundation AI Director and CEO of Insilico Medicine, Dr Alex Zhavoronkov, that increasing lifespan to 150 years is not a fantastic goal. Do you believe that a child born in 2020 will be able to live to 120 or even 150 years?
I want to believe. Being a scientist by education and belief, I have to base my decisions on facts, on understanding the progress of scientific methods in the area. We’ve made an impressive leap in the fields of genetics, biotechnology and medicine in general, and this strengthens my belief. And don’t forget that a substantial part of the success in increasing lifespan is a healthy environment and a healthy lifestyle, so we have to work on this.
In this same paper you mention the potential for mind uploading (transhumanism). Do you believe that this could eventually be a reality, and how does it make you feel personally?
I’ve thought a lot about it. Frankly, it makes me feel frustrated. I think that we associate personality with what we see in a mirror, and for me, it’s hard to detach my character from my body. Nevertheless, this doesn’t mean it is not possible. And, yes, I believe that sooner or later mind uploading will become feasible. The consequences are much harder to foresee.
You’re currently the Head of AI, Healthcare and Life Sciences at DataArt. What are some of the most interesting projects DataArt is currently working on?
We have a project dedicated to new drug development. It’s inspiring how computational methods have developed to fuel and direct the progress in medicinal chemistry and pharmacology. We also do a lot of work on applying AI to extract information from medical texts such as clinical trial reports, medical articles, and specialized forums. It’s hard work, but it takes us closer to the digitalization of healthcare, and I find this exciting.
As an avid book lover, I also need to ask what books you recommend?
- Judea Pearl “Causality: Models, Reasoning and Inference“. The title is self-explanatory – the book is about causal relationships. If (one day) we want to have a true AI we have to teach it reasoning about the cause and effect;
- If you’re going to delve into the causative and practical methods then the fundamental work of Daphne Koller and Nir Friedman “Probabilistic Graphical Models: Principles and Techniques“ will be the right choice;
- We expect powerful AI to be able to understand us. Thus, we have to teach the language to it. Natural Language Processing tackles the problem of natural languages’ comprehension. I have two titles in mind that helped me a lot:
- Yoav Goldberg, Neural Network Methods in Natural Language Processing (Synthesis Lectures on Human Language Technologies), 2017
- Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze An Introduction to Information Retrieval, 2009
- Not sure the next book is dedicated to AI, but it demonstrates a non-standard approach to statistics and predictions which will be useful for any AI researcher: Bertrand S. Clarke, Jennifer L. Clarke Predictive statistics: Analysis and Inference beyond models
- And I would end the list with a sci-fi book: Stanislav Lem, The Star Diaries
Is there anything else that you would like to share about DataArt?
DataArt is an excellent example of the recent trend toward digitalization of almost every aspect of life and activity. This trend increases responsibility in software development because today it’s not only about building a site for a shop, for example, in which case a mistake by the developer will have minimal consequences. Today a developer’s mistake could become a national or worldwide catastrophe if it involves a program controlling the functioning of, for example, a nuclear plant. DataArt’s responsible approach to software development in a broad sense gives me confidence in what we develop, and I am very proud to be part of the company and the work that we are doing.
As for another recent project of hours, last year DataArt launched a prototype application called ‘SkinCareAI’, which analyses skin images to detect early signs of melanoma. Featuring the latest advancements in machine learning (ML) technology, SkinCareAI was developed by DataArt ML expert Andrey Sorokin for the International Skin Imaging Collaboration (ISIC) challenge.
To learn more about some of our other projects and case studies, please go to DataArt’s Healthcare and Life Sciences page.
AI Discovers Smell Genes Linked To Cancer Outcomes
A team of researchers from Oxford University has recently used AI to discover a potential link between colon cancer and the expression of specific smell-sensing genes. As Phys.org reports, researchers from Oxford University and the University of Zurich have recently, assisted by an AI model, discovered that the expression of specific smell-sensing genes within the colon cancer cells indicates a higher probability of worse outcomes.
Genes are expressed when the information that is found within our DNA is used to make molecules like proteins. Gene expression often controls how many proteins are made and when they are made, acting on/off switches. Human beings have approximately 400 genes responsible for our sense of smell, but rather crucially for the study, the genes are also expressed in other parts of the body, aside from the nose. If these smell genes are expressed, it means that the instructions for these genes are being read and carried out. By making changes to the cells, scientists can manipulate the level to which genes are expressed and used.
The study that was recently published in Molecular Systems Biology was lead by Dr. Heba Sailem of the Insitute of Biomedical Engineering and Oxford University. Sailem and fellow researchers examined how cells in the body are organized, aiming to study how cancer leads to the loss of tissue structure in the body. In order to develop effective therapies, scientists must understand which genes play a role in tissue alteration. The research team employed computer vision algorithms to detect changes within the organization of cell samples. The AI model was given image data collected by robotic microscopy, which contains millions of images of colon cancer cells.
The research team then experimented by reducing the expression of every gene in the individual colon cancer cells. After perturbations were applied to the genes and their expression decreased, the researchers found that smell-sensing genes appear to be strongly correlated with how cells align and spread. It appeared that reducing the expression of smell genes could potentially control the spread of cells by reducing their ability to move. On the other hand, cell motility could be increased by having higher expression levels of the smell genes in question.
Sailem explained that the smell genes are like a “sixth sense” that the cancer cells can use to find their way outside of the tumor environment, which is toxic, and spread to other regions of the patient’s body. Sailem went on to explain just how important AI was in making this discovery. The AI model used by the researchers was able to greatly increase the speed the research was carried out at. The AI model, after being trained on a large database of gene functions and appearances, is able to automate the task of identifying certain types of cells within images. Sailem explained:
“Using the developed AI system, we can now learn much more from these experiments and accelerate the identification of genes that alter the structure of tissues in cancer.
CRIPSR (Clustered Regularly Interspaced Short Palindromic Repeats), the gene-editing technology, is the primary way that gene expression levels for the approximately 20,000 genes in the cell are reduced to study how gene expression impacts cancer cells. When combined with advances in gene-editing technology, the research done by Sailem and colleagues could enable new methods of identifying the roles different genes play in different types of cancer, which could enable new kinds of therapies.