Group 1 - AI for Science (AI4S) has become a new paradigm in scientific research, entering an accelerated development phase, utilizing deep learning to solve core challenges in life sciences, materials science, and chemical reactions [7][8][21] - The development of AI4S has led to new collaborative states, with companies either using AI algorithms for contract research or building internal AI platforms for innovation [14][16] - Domestic AI development is increasingly focused on AI+ applications, with large enterprises beginning to consider early-stage layouts [21][25] Group 2 - AI4S applications are addressing three major industry pain points, including efficient information extraction, primary artificial replacement, and deep model predictions for initial assessments [32][33] - Innovation is the main theme of AI applications in the chemical industry, with a focus on six sub-sectors, including strain selection and process optimization in the fermentation industry [37][38] - AI4S applications are characterized by long R&D cycles and high costs, data-driven approaches, and high-dimensional design spaces, which traditional experiments cannot validate individually [36][33] Group 3 - The investment landscape includes companies like JingTai Technology, ZhongKong Technology, and ZhiTe New Materials, which are positioned to benefit from AI4S advancements [4][18] - The AI industry chain has seen significant growth, with upstream and midstream sectors developing rapidly under strong policy support, leading to increased capacity and innovation in hardware and software [25][26][27] - The government is actively promoting AI research cooperation, with initiatives aimed at integrating AI into solving major issues like climate and health [18][20]