Two sets of books for AI-driven pharmaceutical research and development

Since the beginning of this year, those who have been following the AI pharmaceutical industry may have noticed an interesting set of numbers: 18.39 VS 150 - 200 — these are the valuations of two AI pharmaceutical companies in the primary and secondary markets, measured in billions of US dollars. Behind this are two completely different pricing systems. In the primary market, Isomorphic Labs secured a $2.1 billion financing, setting a new record for a single - round financing in the global AI pharmaceutical field and pushing its own valuation to approximately $15 - 20 billion. In the secondary market, Recursion's latest market capitalization is only $1.839 billion. Taking one step forward, the valuations differ by an order of magnitude. What is the logic behind such a gap? Will the gap be narrowed in the future? TechBio companies, which were born from AI algorithms, grew through the implementation of drug research, and are now returning to the expansion of AI applications, are providing their own answers.

From Biotech to TechBio: The Self - Evolution of an AI Pharmaceutical Company

Recently, Insilico Medicine announced progress in its cooperation with Saudi Aramco and launched the Sanity Pipeline system, a streamlined "assembly line" for AI verification of new materials. Once the model runs smoothly, it can reuse proprietary data in various sub - fields and become a general scientific research infrastructure. Previously, the company reached cooperation agreements with global technology giants such as Microsoft, Google, and Amazon (AWS). While obtaining support in computing power, cloud platforms, and model capabilities, it continuously strengthened its AI narrative system. In the past few years, in the eyes of many, Insilico Medicine was more like a Biotech company with an AI facade. Although it has long emphasized its algorithm and model capabilities, what the market has really focused on is always the number of its drug pipelines, the speed of clinical progress, and the data performance of drugs during the verification stage. This also constitutes the initial valuation logic of Insilico Medicine. After rapidly launching 30 candidate drugs and obtaining 13 IND approvals, the company began to return to its "original intention". Relying on its AI - native advantages, it outputs AI platforms, scientific research infrastructure, and industry standards, consolidating its position as a TechBio company. In January this year, Insilico Medicine officially launched the MMAI Gym model training framework and then built a complete ecosystem around it. In March, MMAI Gym completed its first cooperation case and released a lightweight scientific research foundation model by integrating the advantages of the Liquid AI model. In April, MMAI Gym integrated a Benchmark leaderboard portal for scientific research and drug discovery. An infrastructure ecosystem centered on AI training capabilities began to take shape and quickly drove cooperation with Human Longevity in the United States, targeting the trillion - scale longevity research market with the goal of creating "the industry's first large - scale basic model for human aging". The core logic behind this is actually to compete for the right to speak in the underlying infrastructure of AI pharmaceuticals. When a company's products shift from models and tools to training capabilities themselves, it truly integrates into the main artery of the industry. When the unit of innovation time changes from years to minutes, those who can define the evaluation criteria are more likely to gain a higher voice in the industry ecosystem.

From Competing in Drug Pipelines to Emphasizing AI: Returning to the "Original Intention" is the Way to Survive

The transformation from drug pipelines to AI platforms and even industry infrastructure reflects the survival strategies of AI pharmaceutical companies at different life cycles in the capital market. In the primary market stage, all companies that identify themselves as AI pharmaceutical companies want to prove that AI can truly be used in drug development. They rely on pipeline progress, clinical data, and R & D capabilities to attract traditional pharmaceutical funds that truly understand the pharmaceutical industry and are willing to pay for R & D certainty. However, after going public, the market environment begins to change. Especially in the Hong Kong stock market, traditional Biotech companies have long faced valuation suppression. The label of a pharmaceutical company often means a high failure rate, a long cycle, continuous capital burning, and strong uncertainty. On the other hand, AI companies follow a different capital logic: high gross profit, light assets, platform ecosystem expansion, software scale - up, and higher growth premiums. To some extent, this is also a self - evolution that the entire AI pharmaceutical industry is undergoing. As model capabilities gradually converge, future competition may increasingly shift towards platform ecosystems, data systems, scientific research workflows, and industry standards themselves. In May this year, Isomorphic Labs, incubated by Google DeepMind, announced the completion of a $2.1 billion Series B financing led by Thrive Capital, with Google and sovereign funds participating. The company's valuation was pushed up to approximately $15 - 20 billion. This financing set a new record for a single - round financing in the global AI pharmaceutical field. More noteworthy than the financing scale is the valuation logic in the primary market. As of now, Isomorphic Labs still lacks mature clinical pipelines. In other words, the core anchor point for investors to give a high valuation is only the scientific research influence established by AlphaFold and the computing power, models, and ecosystem capabilities provided by Alphabet. What capital is really buying is essentially a technological option for the future. On the other hand, Insilico Medicine is in another typical state. It is one of the first companies to complete the proof - of - concept (PoC) of AI drug efficacy in human patient groups, which means it has answered the ultimate question of "whether AI can truly participate in drug development". However, due to being in the relatively illiquid and conservative Hong Kong stock pharmaceutical sector, the market often subconsciously strips its technological attributes and uses the Biotech framework for pricing, resulting in the neglect of the cross - border empowerment potential of its AI segment. The contrast between these two representative companies vividly shows the obvious "valuation misalignment" of the AI pharmaceutical industry in the primary and secondary markets: The primary market is willing to pay a technological premium for "AI changing life sciences", while the secondary market still calculates risks in the way of traditional pharmaceutical companies.

Behind the Valuation Discrepancy Lies an Opportunity to Reconstruct the Logic

In the long run, this valuation gap will eventually be narrowed. There are two reasons for this: On the one hand, more and more AI pharmaceutical companies in the secondary market are actively strengthening their AI attributes. In addition to Insilico Medicine, at the beginning of this year, Recursion, which is active in the US stock market, also announced cooperation with NVIDIA. Through open - source models, data platforms, and ecosystem output, it continuously emphasizes its AI infrastructure capabilities. On the other hand, the continuously rising valuation anchor points in the primary market will eventually have a reverse impact on the secondary market. As the primary market uses higher valuations to define the technological attributes of AI pharmaceuticals, more and more technology - oriented capital will eventually start looking for corresponding mapped assets in the secondary market. As more and more companies start to strengthen their platform, model, and ecosystem capabilities, the valuation misalignment caused by capital misallocation will not exist in the long term. The industry players truly leading the change are facing a new round of value re - evaluation at the intersection of "performance certainty" and "high technological premium".

This article is from the WeChat official account "Silicon - based Observation Pro". The author is Silicon - based Jun. It is published by 36Kr with authorization.