Group 1 - The core viewpoint of the articles highlights the significant advancements in AI-driven drug development, particularly through the launch of the PROTAC mechanism AI research platform by Dongyangguang Pharmaceutical, which aims to accelerate the rational design and clinical translation of lead compounds, addressing the "undruggable" dilemma [1][2] - The AI pharmaceutical sector is entering a phase of clinical validation, shifting market focus from technological concepts to practical implementation and industrialization pathways, as evidenced by partnerships like NVIDIA's collaboration with Eli Lilly, which involves a $1 billion investment over five years [1] - Dongyangguang Pharmaceutical has developed four core models for molecular screening, generation, PBPK, and retro-synthetic analysis, and has introduced AI-driven models such as HEC-PharmAI, which includes a knowledge base of over 210,000 formulation recipes and thousands of literature and patents [1] Group 2 - The benefits of AI in drug development are evident, as demonstrated by Dongyangguang Pharmaceutical's THRβ agonist project for metabolic fatty liver, where AI pre-screening reduced the number of animal experiments from 100 to about 10, cutting development time by 50% [2] - AI technology can shorten research and development cycles by nearly 40%, save at least 10% in costs, and increase success rates to approximately 14% [2] - Major pharmaceutical companies are adopting a "dual-track" strategy for AI application, focusing on immediate effects while also investing in foundational assets, such as Dongyangguang Pharmaceutical's establishment of an AI research institute and acquisition of related AI assets [2]