Generate:Biomedicines Announces $1B+ Collaboration with Novartis to Develop AI-Designed Protein Therapies

On September 24, Generate:Biomedicines (hereinafter referred to as "Generate") announced a multi-target collaboration with Novartis to discover and develop protein therapies across various disease areas. This collaboration leverages Generate's proprietary generative AI platform, The Generate Platform, which creates potential first-in-class and best-in-class molecules through AI-based optimization and de novo generation.

According to the terms of the agreement, Novartis will pay Generate a total upfront payment of $65 million, of which $15 million will be used to purchase equity in Generate. In addition, Generate will also receive milestone payments exceeding $1 billion, as well as tiered royalties.

Generate Biomedicines, Inc. was founded in 2018 and is headquartered in Massachusetts, USA. It aims to use AI technology to understand the relationship between protein sequences, structures, and their functions, thereby designing entirely novel proteins from scratch and customizing protein therapies to improve drug development in fields such as oncology, immunology, and infectious diseases.

Incubated by venture capital firm Flagship Pioneering, the company conducted two years of foundational research within Flagship's lab division and officially "debuted" in September 2020. Since its establishment, the company has demonstrated remarkable performance in financing, having completed multiple funding rounds with a cumulative amount exceeding $700 million.

In September 2020, Generate Biomedicines received $50 million in funding from Flagship. In November 2021, the company completed a $370 million Series B financing round.

At the end of 2022, Chroma, a generative AI model developed by Generate Biomedicines, Inc., caused a stir in the industry as it was the first large-scale application of diffusion models for precise protein design, capable of generating entirely novel proteins with excellent biophysical and therapeutic properties that do not exist in nature. In September 2023, despite the capital winter, the company completed a substantial $273 million Series C financing round against the trend. Notably, the Series C investors included pharmaceutical giant Amgen and AI computing leader NVIDIA.

In terms of collaboration, prior to this partnership with Novartis, Generate Biomedicines had reached a research collaboration agreement with Amgen in January 2022 to develop protein therapies for five clinical targets, with a potential transaction value of $1.9 billion. Additionally, the company is also collaborating with Roswell Park and MD Anderson Cancer Center to develop protein and CAR-T therapies.

Generate Biomedicines believes that generative biology represents a fundamental shift in therapy development driven by generative AI. This approach not only surpasses the proteins found in nature but also enables the creation of novel proteins to address existing or emerging therapeutic needs, paving the way for a new era of programmable medicines that make drug discovery faster, cheaper, and more flexible.

Currently, Generate Biomedicines has built a rich pipeline involving cancer, immune-related diseases, infectious diseases, etc., with the fastest project already entering Phase I clinical trials.

The collaboration with Generate reflects Novartis' optimism about the future development of AI-driven drug discovery. Only in 2024, Novartis has already achieved multiple investment arrangements in the AI pharmaceuticals sector.

For example, in January this year, Novartis announced a collaboration with Isomorphic Labs, a digital biotechnology company under Google Alphabet, to utilize its AI platform for developing small molecule drugs targeting undisclosed targets. According to the collaboration agreement, Novartis will provide an upfront payment of $37.5 million, with milestone payments reaching up to $1.2 billion. Novartis will also fund part of Isomorphic's research costs, and both parties will collaborate on drug development for three targets. Isomorphic Labs' primary focus is also using AI to achieve protein structure prediction.

Proteins are the executors of life activities, but creating them is a complex task that requires billions of years of evolution. Computational-based protein design aims to shorten this long evolutionary process by automatically designing functional proteins in a programmable manner. Significant progress has been made in this field over the past few decades, but it is still not sufficient to match the complexity and diversity of natural proteins found in nature.

In contrast, generative AI emphasizes designing entirely new proteins from scratch, surpassing the limits of what nature can achieve. Through the Chroma model, Generate Biomedicines can harness the power of generative AI to infer generalizable principles governing the relationships between protein sequences, structures, and functions, thereby rapidly designing novel protein molecules with a wide range of therapeutic properties. This will have a significant impact on medicine, materials science, and other fields.

This collaboration also effectively combines the platform advantages of Generate Biomedicines' The Generate Platform with Novartis' expertise and capabilities in target biology, biologics development, and clinical development. The platform will integrate machine learning with high-throughput experimental validation to create new therapeutic approaches, accelerating the pace of drug discovery and development.

In fact, not only Novartis, but also several multinational pharmaceutical companies including Pfizer, Johnson & Johnson, AstraZeneca, and Merck, have made AI drug discovery an important part of their strategic layout, and are continuously expanding and deepening the scope of cooperation with AI drug discovery companies. Compared to the basic compound screening in the early years, the current collaboration between the two sides has become much richer.

From their respective perspectives, MNCs generally have a positive outlook on the application of AI in drug development, viewing it as a key tool to improve efficiency, reduce costs, and accelerate the time to market for drugs. Meanwhile, MNCs have the capital, data, and extensive experience in drug research and development, but they usually do not develop all the tools themselves. In most cases, they prefer to use tools developed by specialized companies.

For AI pharmaceutical companies, cooperation with MNCs (multinational corporations) represents an opportunity to obtain necessary resources, accelerate drug development processes, and expand business scope. AI pharmaceutical companies often possess deep expertise in a specific technology or disease area. If an AI pharmaceutical company can address the pain points of an MNC with unique technology and help the MNC understand the technical advantages of its AI pharmaceutical platform, it is likely to gain favor from the MNC.

Compared to overseas, China's AI pharmaceuticals industry started relatively late. However, since 2015, numerous AI pharmaceutical startups such as Insilico Medicine, XtalPi, EDDA Technology, StarArray Technology, StoneWise, and SinoAI have emerged. At the same time, traditional pharmaceutical companies like WuXi AppTec have entered the AI pharmaceuticals sector through strategic partnerships or equity financing, while major internet companies such as Baidu, Tencent, and Alibaba have also made moves into this field.

Although AI pharmaceuticals have achieved many results, they are still in the early stages of development, and many issues remain to be resolved, such as regulatory and ethical issues, high-quality data sharing, interdisciplinary talent, and so on. Perhaps AI pharmaceuticals still have a long way to go before the technology reaches the market, but in the future, generative AI will profoundly change the drug research and development model.

References:

Exclusive Interview with Generate: How Did the AI Protein Unicorn, Which Raised Over $700 Million, Come to Be? — VCBeat

Over $10 Billion! MNCs Make Big Bets on AI Drug Development — VCBeat