The Rise of AI in Biotech (Finally)
Applications of AI in drug discovery were mostly within chemical modalities 5-7 year back. Not any longer.
Hi! I am Andrii Buvailo, and this is my weekly newsletter, ‘Where Tech Meets Bio,’ where I talk about technologies, breakthroughs, and great companies moving the industry forward.
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From Chemistry to Biology
I have been tracking the progress of machine learning/deep learning and all realted technologies in pharma industry since 2016, and one trend is apparent: technology is increasingly being adopted by biotech companies, as opposed to a situation several years ago, when the overwhelming majority of ‘AI-driven‘ starups in pharma industry were classical small molecule-focused projects.
In general, the community of AI startups grew rapidly since 2016, and many companies have grown to IPO-stage unicorns, some others went out of business. The majority are doing fine. Currently, my team and I are tracking information about more than 400+ pharma AI companies (we are running probably the most detailed interactive report on the market: “The Landscape of Artificial Intelligence (AI) In Pharmaceutical R&D"), the majority being in small molecules or biologics space, others — in related areas, like retrosynthesis planning, lab assistants, experiment planning software, search engines, etc.
There has been a wave of AI-enabled breakthroughs over the last several years, including novel molecules (small and large) going to clinical trials, AI-enabled advances in clinical trial design and patient stratification, and stuff like that.
There has been a wave of breakthroughs in this area recently, where AI systems helped rapidly discover and develop first-in-class small molecules, and those entered clinical studies -- by such companies Insilico Medicine, Exscientia, BenevolentAI, Recursion Pharmaceuticals, Lantern Pharma, and others (Let me know in the comments if you want me to write about some of the success stories in one of the next newsletters).
So, coming back to small molecules vs biologics story with AI companies.
We did a study in 2022 for 319 actively marketed drug discovery startups developing or applying specialized AI-tools in their research workflow. As you can see, a half of all the companies (49%, 156 startups) are focused on small molecules, while only 20% (64 startups) are involved in discovering/developing biologics drugs (antibodies, vaccines etc). While the disproportion is still there, 20% is way more than it used to be in 2016 (when AI in biologics was primarity a thing of academic labs).
The disproportion towards small molecules is also well illustrated by the amount of VC funding raised collectively by AI-driven startups and scale-ups since 2011 (according to data available in the BiopharmaTrend report)
One interesting conclusion from tracking AI-drug discovery field since 2016 was that apparently, AI made small molecules attractive as an innovative modality again (as opposed to an opinion that chemical modalities are not cool any longer, and only biologics stuff now deserves investor’s attention). The arrival of deep learning in mainstream application after 2012 and all the related progress in AI field, indeed, enabled large scale screening programs unattainable before, enabled phenotipic drug discovery on a new level (analysis of high content data, multiomics, novel targets, etc), and improved many target-based drug design capabilities for known targets (companies like Atomwise, for example).
Another observation, with each year I could see more and more AI-startups in the biologics space: antibodies, peptides, RNA/DNA-based modalities, etc. Below you can review 10 companies in this space that we featured (paid content), but now let’s get to an interview with one of the industry experts about the emerging role of AI in biotech.
RNA Leaders USA 2023: USA Congress
Event Date: Sept. 6, 2023 - Sept. 7, 2023; Location: United States of America, Boston
Website: RNA Leaders USA website;
Exclusive Promo Code for my readers: BT15 (to get 15% off the ticket price)
About the event
RNA Leaders is a high-quality therapeutic conference, focusing on the development of mRNA, RNAi, ASOs, oligonucleotides, vaccines, microRNAs, genome editing, wider nucleic acids & RNA targets.
For investors and pharma, it offers the chance to peruse the science and meet biotechs working on a variety of different product types within an emerging space.
For biotechs, it offers the chance to learn how others are meeting field-wide challenges like regulation and delivery and hear about the work being done beyond their immediate space.
On Biotech Engineering and AI: a Conversation with TeselaGen's CEO, Eduardo Abeliuk
Navigating the dynamic intersection of biology and data science, Dr. Eduardo Abeliuk, the CEO of TeselaGen Biotechnology, invites us on a journey through his career and his company's role in the biotechnology industry. Abeliuk's roots in computational biology research at Stanford University and his rich background as a founder, advisor, board member, and investor have collectively sculpted the robust ethos of TeselaGen.
Amid an era of growth, Abeliuk discusses the profound influences of artificial intelligence and machine learning on biotechnology and outlines a future of intercompany collaboration, fueled by TeselaGen’s innovative software platform. He highlights the merger of biology and data science as a game-changing development, which TeselaGen aims to pioneer by transforming bioengineering with AI-driven tools. The democratization of advanced studies, he believes, will diversify the research landscape and usher in a new wave of biotech innovation.
Abeliuk reflects on the transformative potential of TeselaGen's recent partnership with NinthBio and its implications for drug discovery, sustainable agriculture, and industrial biotechnology. This interview provides a rare glimpse into the intricacies steering TeselaGen towards an exciting biotech horizon:
Andrii: Eduardo, can you share your journey leading up to becoming the CEO of TeselaGen Biotechnology, and how your experiences have shaped the company's current direction? I began my journey in biotechnology right out of graduate school, fascinated by the potential that this technology had to revolutionize our lives.
Eduardo: Prior to TeselaGen, I did research at Stanford University, where I was a member at the Information Systems Lab (ISL) and at the Beckman Center for Molecular and Genetic Medicine, where I worked on computational biology. I spent many years myself designing biological experiments and building DNA. While planning the experiments, I found myself wasting significant time performing tasks that I felt could be automated with good software. That was a strong motivation to start TeselaGen and to develop an enterprise-grade software that could speed-up R&D in Biotechnology. In addition, over the course of my career, I've been fortunate to be involved with various companies in several capacities – from founding two other successful companies prior to TeselaGen, to serving in advisory roles, sitting on boards, and even participating as an angel investor in a few ones. Each of these experiences provided me with a unique perspective and set of skills that I've carried into TeselaGen. As a founder, I learned the ins and outs of building a company from the ground up. This experience taught me about the importance of a strong vision, the need for resilience in the face of adversity, and the crucial role of a dedicated, talented team. It also gave me first-hand experience in the excitement and challenges of bringing innovative ideas to market. In my advisory roles and as a board member, I gained broader industry perspectives and developed a deep understanding of strategic decision-making. These roles also taught me the importance of good corporate governance, effective communication, and stakeholder management. My experience as an angel investor helped me understand the business from a financial perspective. I learned to assess the viability and potential of startups, to understand market trends and investment risks. This experience has given me valuable insight into what drives growth, the importance of financial management in ensuring the long-term sustainability of a company, and to build relationships with other investors. Bringing all these experiences to TeselaGen, I believe I bring a multi-faceted understanding of how to foster innovation, build and motivate a team, make strategic decisions, and maintain financial health. Above all, these experiences have instilled in me a deep appreciation for the transformative potential of biotechnology combined with computational biology and I am committed to leading TeselaGen in harnessing this potential to its fullest.
Andrii: In light of the recent partnership with NinthBio, could you elaborate on the motivations and objectives behind integrating the Homology Path design algorithm with TeselaGen's software platform?
Eduardo: Absolutely. As a company, our aim is to remain at the forefront of innovation in synthetic biology and biotechnology, and to continuously expand the capabilities of our software platform. We've been following NinthBio's work with the Homology Path design algorithm, and were excited by the value that it could provide to our users. The algorithm's capabilities are set to enhance our platform's design capabilities, enabling our customers to streamline their workflows, reduce costs, and ultimately, get to market faster. We believe that by integrating NinthBio's algorithm, we can provide our users with an even more advanced toolset for synthetic biology that can open up new possibilities for innovation and discovery. This partnership is also about synergy - bringing together cutting-edge technologies to create a more powerful, comprehensive solution. Ultimately, we want to continue to empower researchers and companies in the biotech space, helping them unlock the full potential of biotechnology.
Andrii: The biotechnology industry is witnessing unprecedented growth and evolution, especially in AI and machine learning. As a key player in this field, where do you see the industry heading in the next 5-10 years, and what role do you envisage TeselaGen playing in this evolution?
Eduardo: Indeed, the biotechnology industry is in the midst of an exciting period of transformation and growth. Over the next 5 to 10 years, I believe we'll see several key trends shaping our industry. Firstly, the integration of artificial intelligence and machine learning in biotechnology is expected to continue and accelerate. These technologies are enabling faster, more accurate analysis of complex biological data, which in turn can help us design better drugs, products, and processes. Secondly, the use of synthetic biology will become increasingly mainstream as its potential continues to be realized. Synthetic biology could provide solutions to some of the most pressing problems we face, from sustainable manufacturing to food security and disease treatment. As a result, I anticipate that investment and interest in this area will continue to grow. Lastly, I believe that we will see a greater emphasis on cross-disciplinary and intercompany collaboration in the biotech industry. Tackling complex biological challenges requires expertise from a variety of fields and companies. As such, I expect to see more partnerships between biotech companies, research institutions, and technology firms. As for TeselaGen's role in this evolution, our goal is to remain at the forefront of these trends. Through our innovative software platform, we are pioneering the application of advanced computational tools and AI in synthetic biology. Moreover, we are committed to fostering collaboration and openness in the biotech industry. By creating a platform that facilitates the design-build-test-learn cycle in bioengineering, we hope to enable more intercompany collaboration and accelerate the pace of innovation in biotech. In summary, we envision TeselaGen as a key facilitator in the evolution of the biotech industry, driving innovation, collaboration, and progress towards a future where biotechnology and synthetic biology's full potential can be realized for the benefit of all.
Andrii: The integration of these technologies seems to blur the line between biology and data science. Can you share your thoughts on the increasing intersection of these two fields and how TeselaGen aims to pioneer this space?
Eduardo: Certainly. The intersection of biology and data science is one of the most exciting developments in modern science. In many ways, we can think of biology as information science. DNA, after all, is a form of data storage, and the processes of life involve the transfer and interpretation of this data. The tremendous volume and complexity of biological data require sophisticated analytical techniques to decipher. This is where data science, and specifically AI and machine learning, come into play. These technologies can handle the scale and complexity of this data, identifying patterns and insights that would be beyond the reach of traditional analysis. The integration of these fields is driving a new era of precision in biotechnology. It's enabling us to develop targeted treatments, improve diagnosis, and predict disease progression in ways we couldn't before. Additionally, in the field of synthetic biology, the combination of biology and data science is accelerating the design-build-test-learn cycle, making the development of new bio-products more efficient and predictable. At TeselaGen, we're at the vanguard of this exciting intersection. Our software platform is designed to leverage the power of data science and AI in the realm of biology. On one hand we empower scientists to generate much more data at faster speed and lower costs. On the other hand, these datasets can fuel features like predictive design and advanced data analysis, transforming the way bioengineering is done. We're using the power of data science not just to understand biological processes, but to design them, pushing the boundaries of what's possible in synthetic biology. As we move forward, we aim to continue pioneering in this space. Our goal is to empower bioengineers and researchers with the tools they need to unlock the full potential of synthetic biology, creating a future where the boundaries between biology and data science become increasingly intertwined.
Andrii: How do you think this new development will democratize research in biotechnology, enabling more research groups to conduct advanced studies in-house, which were traditionally outsourced?
Eduardo: Great question. Democratizing research in biotechnology is at the heart of what we aim to achieve at TeselaGen. By integrating advanced technologies into our software platform, we aim to empower more research groups to conduct high-level studies in-house, which were previously outsourced to a few institutions due to the complexity and technical expertise required. One of the biggest challenges for smaller research groups is having access to the same advanced tools and technologies that larger, better-funded organizations do. With our software platform, we aim to level the playing field. Regardless of their size or funding, research groups can access state-of-the-art tools to design and optimize their bioengineering workflows. Furthermore, conducting studies in-house brings several benefits. It can increase the speed of innovation, and allow for greater control over the research process. Researchers can iterate quickly, adapt their designs in real-time, and keep proprietary work in-house. Finally, by enabling more research groups to conduct advanced studies, we're fostering a more diverse and innovative research landscape. Different groups will bring different perspectives and ideas, leading to a greater variety of solutions and innovations in biotechnology. Overall, we believe that democratizing access to advanced biotechnology tools will stimulate a new wave of innovation, bringing us closer to realizing the full potential of synthetic biology.
Andrii: What kind of impact do you expect this partnership to have on areas like drug discovery, sustainable agriculture, and industrial biotechnology? Could you share some potential use-case scenarios?
Eduardo: Certainly. The partnership between TeselaGen and NinthBio, particularly with the integration of the Homology Path design algorithm, has the potential to dramatically impact various fields, including drug discovery, sustainable agriculture, and industrial biotechnology. In the realm of drug discovery, our enhanced platform can speed up the process of developing new drugs. The improved efficiency in designing and building combinatorial protein libraries can allow researchers to more quickly generate and test new drug candidates. In sustainable agriculture, the technology can be used to engineer plants or soil microbes to enhance crop resistance to pests, diseases, or environmental stressors. For instance, a research group could use our platform to design a modification in a crop plant's genetic code that makes it more drought-resistant, potentially contributing to food security in the face of climate change. Industrial biotechnology stands to benefit as well. The platform can be used to optimize the production of bio-based chemicals, biofuels, or bioplastics. A biofuel company, for example, could engineer microbes to improve the production efficiency of bioethanol, contributing to the development of more sustainable energy sources. In all these cases, the advanced capabilities of our platform, enhanced through our partnership with NinthBio, can help to accelerate innovation, make research and production processes more efficient, and ultimately contribute to the advancement of biotechnology and its application for societal benefit.
Andrii: Lastly, on a personal note, leading such a cutting-edge company must come with its fair share of challenges and rewards. Can you share one of each that you've encountered on this journey?
Eduardo: Certainly, leading TeselaGen has indeed been a journey filled with both challenges and rewards. One of the biggest challenges has been navigating the rapidly evolving landscape of biotechnology. The industry is in a constant state of flux and growth, with new breakthroughs and technologies emerging at a fast pace. Keeping TeselaGen at the forefront of this wave, ensuring we stay innovative and competitive, requires a constant willingness to learn, adapt, and take risks. It also involves the challenge of integrating complex biological knowledge with advanced computational techniques, creating a product that is both scientifically robust and user-friendly. Despite the challenges, the rewards have been immense. One that stands out to me is seeing the real-world impact of our work. For instance, knowing that our platform is being used to accelerate the next generation of cell and gene therapies and drive advancements in industrial biotechnology is incredibly fulfilling. I take great pride in the fact that our work at TeselaGen is contributing to solutions for some of the world's most pressing problems, from disease treatment to climate change. Another rewarding aspect has been building and leading a team of highly talented individuals who are equally passionate about harnessing the power of synthetic biology. Seeing this team overcome obstacles, innovate, and continuously strive to make our platform better is an experience that continues to inspire me on this journey.
10 Companies Applying AI for Biologics Discovery
When we hear “AI in drug discovery”, there is a high chance our imagination would draw a small molecule discovery in the first place. Indeed, as it was captured and analyzed in the report The Landscape of Artificial Intelligence (AI) In Pharmaceutical R&D roughly half of more than 300 analyzed AI-driven companies focus on small molecules as a therapeutic modality. This biggest category is then followed by smaller groups of biologics, biomarkers and therapies as an R&D focus. The success of small molecule discovery coupled with AI already demonstrates performance with some “AI-inspired” drug candidates entering the clinical trials, like Relay Therapeutics with their precision oncology small molecules in clinical trials for previously ‘undruggable’ targets, or Exscientia with their small molecule candidate to treat obsessive-compulsive disorder, which began human testing in a phase I trial in 2020 in collaboration with Sumitomo Dainippon Pharma.
At the same time, the number of companies using AI for biologics discovery is also continuously growing. In BiopharmaTrend report there are already 65 selected AI-driven companies focusing on biologics.
According to the definition by FDA, biological drugs include vaccines, blood and its components, somatic cells, gene therapy, recombinant therapeutic proteins and even tissues. Some narrower examples include therapeutic antibodies for cancer treatment or even COVID-19 vaccines, but all biologics share the same composition “requirement” (sugars, proteins, nucleic acids, their combinations or living entities) and the isolation origin (humans, animals, plants, microorganisms).
Below we list ten notable companies applying AI for designing biologics drugs:
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