AI Biotech Spring Arrives? Two $500M+ Pharma Deals Closed in One Day Signal Industry Maturation

On December 5, Sanofi and AbbVie announced new collaborations with AI pharmaceutical companies on the same day.

Sanofi Signs Multi-Year Research Collaboration Agreement with French Pharmaceutical Company Aqemia to Develop Small Molecule Candidates for Several Undisclosed Therapeutic Areas. Aqemia will leverage its artificial intelligence (AI) platform to design drug molecules and use physics-based computations to generate data, accelerating drug discovery. Sanofi will be responsible for wet lab research, development, and commercialization activities. Under the terms of the agreement, Aqemia is eligible to receive up to $140 million in upfront and milestone payments across all programs.

AbbVie and BigHat Announce Research Collaboration to Discover and Develop Next-Generation Therapeutic Antibodies in Oncology and Neuroscience Fields

After weathering the storm of controversy, the surviving AI pharmaceutical biotechs are no longer pursuing the "pipeline sale" path to monetization but are instead moving towards "research and development collaboration." Has their spring arrived?

Looking back at the two collaborations, it's not difficult to see that the two Biotechs involved in this partnership are still focused on the early-stage areas of drug molecule/antibody development and design.But unlike the early "selling pipelines" approach, the terms used in collaborations with pharma are all about large-scale, long-term, and in-depth cooperation characterized by "multi-year," "multi-field," and "multiple therapeutic targets."Taking Sanofi's "AII in AI" as an example, whether it is the multi-year, multi-target research collaboration with Insilico Medicine and Aqemia, or the cooperation with BioMap based on large models to jointly develop drug discovery models, all are long-term, wide-ranging strategic collaborations.

For Biotech, strategic collaborations are no longer one-off deals but deep partnerships that don’t determine success or failure in a single stroke, allowing room for trial and error as well as improvement. On one hand, Biotech will gain access to pharma’s resources, cooperation, and potential data-sharing opportunities, which will help update and iterate its own platforms. As seen in the collaboration with Aqemia, Sanofi will leverage its global R&D expertise while solely taking charge of wet-lab research, development, and commercialization activities. On the other hand, with multiple efforts progressing simultaneously, if a single drug pipeline succeeds, Biotech can also secure royalties and corresponding milestone payments.

For Pharma, deep cooperation is undoubtedly a long-term investment with less risk and lower cost. The key question is, how can AI pharmaceutical Biotech stand out and enter the sights of Pharma?

Unlike AI platforms that require experimental data for training, Aqemia utilizes research from Oxford University, Cambridge University, and ENS/CNRS over the past 12 years.Unique Quantum Physics Algorithm, generate its own data, and handle drug discovery projects from the initial stages. Aqemia claims that its deep physics algorithms can achieve 10,000 times faster speeds, effectively guiding generative AI to rapidly and scalably discover drug projects, introducing the innovative engine Launchpad. In collaborations with Sanofi, Janssen, and Servier worth several million euros, multiple drug pipeline research initiatives have been launched based on Launchpad results, with a particular focus on oncology and cancer immunology.

BigHat's Milliner™ platform integrates machine learning technology,High-Speed Wet Lab Based on Synthetic BiologyIntegrated into a complete antibody discovery and engineering platform. After platform iteration and machine learning model design, BigHat can utilize synthetic biology technology for multiple tests in its own laboratory. Using cell-based or other functional assays, BigHat replicates in vivo disease processes to measure the biophysical properties of each variant and their impact on disease activity, and then updates the model and iterates predictions.

AI-driven Biotech companies are no longer choosing blind expansion but instead selling models and seeking collaborations; Pharma companies, on the other hand, are both building their own AI teams and actively collaborating with various outstanding and promising AI-driven Biotechs. As transactions in the sector return to rationality, this marks both a post-bubble phase of sensible development and the start of destigmatization and healthy competition.

Looking back at the mid-to-late 2010s, Google DeepMind successively developed AlphaGo, AlphaZero, and AlphaFold1. Particularly, AlphaFold2 marked a significant leap in protein spatial prediction. Artificial intelligence and machine learning solved problems that had plagued the pharmaceutical industry for centuries, elevating them to an almost godlike status. For a time, people believed that AI and ML could solve all existing issues within the pharmaceutical industry, even advancing to the point of drug generation.

The widespread talk, myths, and hype have created a massive bubble in AI-driven drug discovery — the field is booming, capital is pouring in, companies are emerging, and the stories about pipeline discoveries and technological breakthroughs are dazzling. But the pitfall was there from the start: in practical applications, how much of an impact can AI really have on the pharmaceutical industry? Beneath the myth of cost reduction and efficiency improvement, how high is the success rate of drugs reaching Phase II or Phase III clinical trials? In no time, the bubble of AI-driven drug discovery has burst, turning "myth" into "stigma."

The bursting of the bubble is also a process of track selection and iteration. The FDA and EMA have put forward clear definitions for AI/ML, and the current consensus is: AI/ML is only an auxiliary tool that can help the pharmaceutical industry develop.

Looking back at the companies in the current track: fundamental algorithm innovation, AI and genetics innovation jointly driving drug discovery, AI drug discovery integrated with wet labs, AI applied to clinical trial development, AI chemical route design + digital lab management... After returning to rationality, AI/ML is gradually moving towards more vertical, specialized, and industry-oriented applications.

Previously, Dr. Tao Du, former senior review officer of the FDA and chairman of Shenzhen Egret Pharmaceuticals, mentioned in an exclusive interview with Vbdata that the role of AI/ML in the future pharmaceutical industry will become increasingly significant.Therefore, AI technology in the pharmaceutical industry is not overheated, but rather not mature enough, or it can be said that the coverage of China's AI pharmaceutical industry is still not wide enough.From the current situation, AI is still in an early stage in the pharmaceutical industry. In the future, through close integration between various AI service agencies and traditional pharmaceutical companies, the application of AI in the pharmaceutical R&D process will become increasingly widespread.

This year, Insilico Medicine and XtalPi have submitted their IPO applications, striving for a listing on the Hong Kong Stock Exchange. AI pharmaceutical companies moving towards industrial applications and win-win cooperation are embracing more possibilities. Let us remain rational and stay full of anticipation.