The research report issued by CITIC Construction Investment highlights the burgeoning growth in the realm of AI-powered drug development. This expansion is underpinned by continual algorithmic refinements and enhancements in computational capabilities, which pave the way for its widespread application. Traditional drug development is notorious for its protracted timelines, exorbitant costs, and low success rates. With the successive approval of new drugs targeting established molecules, the challenge of discovering novel drugs escalates. However, recent strides in AI, especially deep learning techniques, have propelled their integration into various stages of drug development, including target identification, molecular design and optimization, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) forecasting, crystal structure prediction, and synthetic pathway screening. Moreover, the investment in AI for the development of macromolecular drugs has been steadily rising. AI-driven drug discovery (AIDD) is poised to revolutionize the initial phases of drug discovery, substantially enhancing screening efficiency and success rates. The escalating investment in computational (dry) experiments, coupled with the burgeoning demand for laboratory (wet) experiments, is anticipated to fuel sustained growth across the downstream industry chain.