HK Stock Market Move | METIS TECHBIO-P (07666) rose nearly 7% in morning trading. The core competitiveness of Biological AI lies in real experimental data.
METIS TECHBIO-P (07666) rose nearly 7% in morning trading. As of press time, it was up 5.45% at HK$17.8, with a turnover of HK$69.1574 million.
METIS TECHBIO-P (07666) rose nearly 7% in morning trading. As of press time, it was up 5.45% at HK$17.8, with a turnover of HK$69.1574 million.
On the news front, on September 19, the 29th Beijing-Taiwan Science and Technology Forum was held in the Beijing Economic-Technological Development Area. Lai Caida, co-founder, chairman and CEO of METiS Therapeutics, was invited to deliver a keynote speech titled "The Era of Programmable Biology: AI's New Frontier." Rather than "how big the model is," Dr. Lai Caida emphasized another point: what biopharmaceutical AI ultimately competes on is whether it can continuously obtain high-quality experimental data and translate that data into model capabilities.
It is understood that Lai Caida used a "three-layer cake" to describe the technology architecture of AI drug discovery. The bottom layer, and also the thickest first layer, is the dry-lab and wet-lab data platform. General-purpose large models can obtain massive amounts of text from the internet, but the data truly needed for drug delivery, protein, nucleic acid and cell research comes largely from the laboratory: which tissue an LNP enters, how different lipid structures affect expression, and how activity changes after protein sequence alterations must all be obtained through experiments. The second layer consists of vertical models generated using this data. The third layer is large language models and Agents.
Since the release of NanoForge, the world's first AI nano-delivery platform, METiS Therapeutics' pioneering new R&D paradigm of "dry lab + wet lab + intelligent agents" has fully covered the complete chain of AI protein generation, in vivo delivery technology and next-generation immunotherapy development, forming a continuously learning R&D closed loop and a nano-delivery data barrier, achieving continuous breakthroughs in extrahepatic delivery, and providing underlying support for the exploration of AI precision delivery and cell programming.
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