On November 4, 2025, Frost & Sullivan officially released the 2025 China AI4LS Industry Blue Paper (hereinafter the “Blue Paper”), its first panoramic industry research report focused on AI for Life Sciences (AI4LS) in the Chinese market. Based on continuous tracking of technological pathways, enterprise innovation capabilities, market dynamics, and capital activity, the report highlights DIVAMICS as a representative enterprise in scientific research and AI-enabled drug discovery innovation.
Frost & Sullivan notes in the report that DIVAMICS has built a differentiated foundational technology system through the deep integration of artificial intelligence, quantum mechanics, and molecular simulation algorithms. This architecture aligns closely with the multidisciplinary and computation-driven paradigm that defines the AI4LS era, providing a technical foundation for enhancing R&D efficiency in life sciences and demonstrating transformative potential across key stages of drug development.
The Blue Paper presents a comprehensive overview of the AI4LS ecosystem, covering the evolution of scientific research paradigms, the composition of the technical stack, and application scenarios. It further outlines future growth drivers and high-potential market segments.
According to the report, China’s AI for Science (AI4S) market reached RMB 4.7 billion in 2024, is projected to grow to RMB 6.0 billion in 2025 and is expected to reach RMB 149.9 billion by 2070, highlighting strong long-term development prospects. As this trend accelerates, AI is increasingly permeating drug discovery, synthetic biology, gene sequencing, materials science, and energy storage.
The life sciences sector is also undergoing a major paradigm shift toward data-driven research. AI technologies offer verifiable and reproducible advantages when processing large-scale, complex biological data, making it possible to improve on traditional R&D models that historically relied heavily on empirical methods and iterative experimentation.
In line with this transition, AI4LS is forming a new research infrastructure that includes high-throughput data acquisition, intelligent algorithm modeling, automated experimental validation, and knowledge graph construction, with applications gradually expanding across drug discovery, genomics, and synthetic biology.
In its analysis of industry transformation needs, the Blue Paper summarizes the primary challenges facing the life sciences sector: rising experimental costs, lengthy R&D cycles, and data-scale constraints that collectively form the sector’s longstanding “impossible triangle.”
In its analysis of industry transformation needs, the Blue Paper summarizes the primary challenges facing the life sciences sector: rising experimental costs, lengthy R&D cycles, and data-scale constraints that collectively form the sector’s longstanding “impossible triangle.”
To accelerate R&D timelines and workflows, DIVAMICS has developed an AI–molecular dynamics–experimental data closed-loop feedback mechanism. Through iterative cycles of computational prediction and experimental validation, the mechanism continuously optimizes AI models and simulation accuracy. AI-accelerated molecular dynamics simulations (including conformational ensembles and transient pocket identification) and AI-driven predictions of allosteric sites are used to generate hypotheses that are submitted to experimental validation. The resulting structure–function data are then fed back into the AI models for retraining, parameter refinement, and feature optimization, ultimately forming a continuously improving “dry–wet” hybrid R&D loop.
In improving data research efficiency, DIVAMICS’ Biotrajectory AI system—built on a transformer architecture and enhanced by large language models (LLM) and natural language processing (NLP)—integrates extensive literature, patent, and clinical databases. This enables rapid exploration and synthesis of molecular design and modification strategies at speeds far surpassing traditional workflows, significantly improving early-stage research success rates.
Taken together, Frost & Sullivan concludes that DIVAMICS has established a closed-loop system that spans data generation, algorithm iteration, and drug design through its molecular dynamics platform and Biotrajectory AI trajectory database. This system supports a wide range of drug modalities, including small molecules, large molecules, nucleic acids, antibodies, and peptides. The company’s “platform + pipeline” dual-engine model provides strong technical integration capabilities and facilitates coupling across key stages of AI-enabled drug discovery and clinical development.
In the section analyzing application scenarios, the Blue Paper further describes the role of AI in improving molecular design quality, optimizing development workflows, and increasing overall success rates.
