Venture(US:VEMLY) 3 6 Ke·2026-01-09 05:47Core Insights - The article discusses the transformative impact of AI in the field of science, particularly in drug development and related industries, highlighting the significant advancements made by companies like JingTai Technology in AI-driven pharmaceutical innovations [1][3]. Group 1: AI in Pharmaceutical Development - AI in pharmaceuticals has reached a "fruit-bearing" stage, with companies like JingTai securing major partnerships and contracts, such as a $3.45 billion collaboration with Eli Lilly and a nearly $60 billion agreement with DoveTree [5][11]. - The success of AI in drug development is evidenced by JingTai's MTS-004 oral disintegrating tablet reaching Phase III clinical trials, marking it as the first AI-enabled new drug in China to achieve this milestone [5][11]. - AI's ability to enhance drug discovery processes has shown efficiency improvements ranging from 20% to 80% in preclinical drug discovery [8][10]. Group 2: Future Opportunities in AI for Science - The conversation emphasizes the potential for AI to extend its capabilities beyond pharmaceuticals into fields like chemistry, materials science, and physics, suggesting that AI could drive foundational innovations in these areas [5][6]. - The "14th Five-Year Plan" indicates a strategic focus on high-tech industries, including quantum technology and bio-manufacturing, which could benefit from AI integration [6][11]. - The discussion highlights the importance of merging technological innovation with industrial applications to maximize the impact of AI in scientific research [6][11]. Group 3: Data as a Strategic Asset - The article notes that data will be a crucial asset in the AI-driven biopharmaceutical sector over the next 3-5 years, with a focus on improving data collection and quality [10][12]. - JingTai is actively working on building a competitive advantage through automated experimental platforms to enhance data acquisition and standardization [12][29]. - The importance of high-quality, rapidly feedback-capable data is emphasized, as it is essential for training AI models effectively [33][34]. Group 4: AI's Role in Drug Development Processes - The integration of AI in drug development processes is seen as a way to optimize both sequence design and modification design in nucleic acid drugs, allowing for more efficient and innovative drug development [41][44]. - The article discusses the potential for AI to redefine traditional drug development workflows, leading to new discoveries and commercial opportunities in emerging modalities [46][47]. - The need for a collaborative approach in drug development, where AI assists in both the design and clinical phases, is highlighted as a key to future success [14][41]. Group 5: Cross-Industry Innovations - The article suggests that AI's applications are not limited to pharmaceuticals but extend to materials science, energy, and agriculture, indicating a broad potential for innovation across various sectors [47][48]. - The shared technological foundations across industries allow for quicker adaptation and value realization in new fields, although the speed of data feedback and validation processes may vary [48][51]. - The potential for AI to enhance productivity in sectors like bio-manufacturing and quantum computing is also discussed, positioning China as a leader in these emerging industries [51].