Comprehensive Company
In the field of AI for Science, comprehensive companies serve as the core hubs that break down disciplinary boundaries and connect the technical chain. They cover multiple research fields such as biomedicine, materials science, physics, and climate, deeply integrating data governance, core algorithms, and high-performance computing resources, and providing AI-enabled solutions that cover the entire process and are adaptable to various scenarios for scientific research teams.
These enterprises mainly need to address two major industry challenges:
One is technological fragmentation. Research work often requires frequent switching between different tools and platforms, facing issues such as inconsistent data formats and difficult interoperability of algorithm interfaces. Comprehensive companies standardize and platformize various resources, enabling AI capabilities to span all stages from basic research to technological transformation.
The second factor is the disciplinary barriers. Algorithms in the field of biomedicine can be applied to material design, and the computing power for climate simulation can also support quantum chemical calculations. Thus, cross-disciplinary technology reuse becomes possible.
Specialized AI firms may excel in specific algorithms or databases, while comprehensive companies offer a full suite of AI research solutions. Whether it's data processing and algorithm validation in the foundational research phase or process optimization and efficiency gains in the technology transfer stage, they can identify precise empowerment entry points.
Overseas comprehensive company
Overseas comprehensive companies are at the forefront in terms of technological leadership and ecosystem construction. Among them, some start with algorithm breakthroughs, while others begin with data platforms or computing infrastructure. However, ultimately, they all aim to build a cross-disciplinary AI research infrastructure system. (The following companies are listed randomly and there is no specific order or priority.)
Google DeepMind is the pioneer of AI4Science, establishing its position in the industry through breakthroughs in multi-disciplinary algorithms.
It covers protein structure prediction in biomedicine, quantum simulation in physics, molecular design in materials science, and reasoning problems in mathematics. Representative technologies include the AlphaFold series of algorithms and the FermiNet quantum simulation algorithm. DeepMind not only focuses on developing individual technologies, but also pays attention to promoting fundamental changes in basic research through algorithm innovation, providing core underlying technology support for global research institutions.
Official website link：https://www.deepmind.com/
OpenAI for Science extends the mature large-model technology to interdisciplinary research scenarios.
Its core areas include literature analysis and target discovery in biomedicine, experimental design in materials science, and assistance in solving mathematical problems. Leveraging the advantages of the GPT series of large models, the company focuses on providing capability support at the algorithmic level of the research process. It is dedicated to exploring the auxiliary role of AI in key aspects such as literature review, experimental design, and problem deduction, thereby providing efficient tool systems for researchers.
The Science-related business has not yet set up a separate station. The latest updates can be found on the main OpenAI website：https://openai.com/
The Microsoft AI4Science Lab adopts an integrated model of "algorithm + computing power + expert team" to enter the market.
Its core research areas cover climate and weather simulation in climate science, multi-scale modeling and molecular material computation in materials science, as well as molecular design assistance and cross-application of chemical biology in biomedicine. This laboratory relies on the Azure cloud and high-performance computing (HPC) large-scale computing power platform, integrates experts from multiple fields such as machine learning, computational physics and molecular biology, and provides a full-chain research service from "machine learning - simulation - system engineering", aiming to promote the deep integration and innovative application of algorithms and computing power.
Official website link：https://www.microsoft.com/en-us/research/lab/microsoft-research-ai4science/
The core positioning of Palantir Foundry is that of a builder of scientific research data platforms.
Focusing on the management of research data across the fields of biomedicine, materials science and earth science, integrating multimodal resources such as experimental data, literature and patents. The technical focus of this platform is not on algorithm development, but on building a standardized data processing and sharing system, providing high-quality and standardized data basis for AI model training, thereby addressing core industry challenges such as the dispersion and inconsistent formats of research data.
Official website link：https://www.palantir.com/foundry
SandboxAQ focuses on the integration of quantum and AI technologies.
The core areas include quantum chemical simulation in physics and molecular quantum system analysis in biomedicine. It develops an exclusive algorithm framework integrating quantum computing principles and AI models, creating a molecular quantum simulation algorithm and software platform. Through the unique path of "quantum + AI", it provides efficient solutions for complex molecular system research computations.
Official website link：https://www.sandboxaq.com/
Domestic comprehensive company
Domestic comprehensive companies have developed their own distinctive approaches in terms of scenario implementation and full-chain empowerment. On one hand, they draw on overseas technological concepts; on the other hand, they closely integrate with domestic research institutions and industrial actual needs, conducting in-depth customization and adaptation.
Baidu focuses on the core scenarios of biomedicine in the AI4Science field.
Its core areas include HelixFold-S1 protein structure prediction, mRNA sequence design, and peptide and antibody drug development. Baidu independently developed core algorithms such as HelixFold3 and LinearDesign, and combined with the computing power support of PaddleHelix's exclusive bio-computing platform, it has constructed an "algorithm + computing power" integrated service system, providing AI-enabled support for the entire process of drug development from target discovery to molecular design.
Propeller PaddleHelix：https://paddlehelix.baidu.com/
Alibaba DAMO Academy adopts the "PAI platform + Hua Guang NPU" as its core architecture.
The core area focuses on "intelligent + computing" data science, covering cutting-edge directions such as AI infrastructure, AI for Science, and AI applications. The research and industrialization implementation of this area involve technical studies and applications in fields like medical AI, decision-making intelligence, embodied intelligence, genetic intelligence, video technology, RISC-V and its ecosystem, as well as computing technology, among others.
Damo Institute：https://damo.alibaba.com/
PAI 平台：https://www.aliyun.com/product/bigdata/learn
ByteDance Seed - AI for Science team focuses on exploring cutting-edge technologies for scientific computing.
Its core areas cover multimodal biological foundational large models, encompassing three major directions: proteins, DNA, and RNA, as well as quantum chemistry and AI molecular dynamics. The company has developed the Protenix series of multimodal biological molecule structure large models and the AI molecular dynamics simulation platform, establishing an integrated R&D loop of "machine learning - quantum chemistry - simulation", which supports efficient development in the fields of drugs and materials.
ByteDance Seed Official Website：https://seed.bytedance.com/zh/direction/ai_for_science
Tencent's Life Sciences and AI Laboratory has adopted a dual-drive strategy centered on "algorithm + data" in the field of AI4Science.
Its core areas include the iDrug drug discovery platform in the field of biomedicine, as well as the data-driven SPDB single-cell proteome database. Tencent not only independently develops key algorithms for drug research such as molecular generation and protein structure prediction, but also systematically builds professional databases in the field of biomedicine. Through the dual drive of "algorithm tools + data resources", it covers the entire chain of drug discovery and provides closed-loop services for research teams, ranging from data retrieval to algorithm verification.
iDrug platform：https://drug.ai.tencent.com/
SPDB Database：https://scproteomicsdb.com/
Shenshi Technology has pioneered a unique paradigm of "multi-scale modeling + machine learning + high-performance computing".
Its core areas cover drug computational design and target development in biomedicine, material design in materials science, and battery research in the field of new energy. The company has launched a series of products such as the Bohrium® scientific cloud platform, the Hermite® drug computational design platform, and the RiDYMO® hard-to-target drug development platform. Through its independently developed Deep Potential series of methods, it has achieved an exponential increase in the efficiency of molecular dynamics calculations, and has constructed an AI scientific research infrastructure system spanning multiple industries.
Official website link：https://www.dp.tech/
Bohrium 平台：https://bohrium.dp.tech
Eagle Valley Information focuses on the integration of data governance and AI applications.
The core areas cover three major fields: pharmaceuticals, chemistry, and biomedical science. It uses the electronic laboratory notebook InELN as its core product matrix and builds a bottom-level system for research data. The scientific AI model InAI Pro2.0 released in 2025 is based on DeepSeek technology and possesses the capabilities of AI intelligent question answering, experimental plan design, weekly reports, patent generation, and automatic creation of application materials and draft copies. It achieves a full-loop closed process of "data governance - algorithm application - result output".
Official website link：https://www.integle.com/
As the national representative in the field of AI4Science, the Chinese Academy of Sciences focuses on the construction of scientific intelligent basic models and platforms in multiple disciplines such as astrophysics, energy materials, and mechanical engineering.
It has launched the "Rocky - One-stop Research Platform", integrating nearly 300 scientific computing tools, including the Literature Compass, the Tool Dispatch Desk, the Innovation Evaluation, and the Intelligent Agent Factory, which constitute four major scientific intelligent entities. Its intelligent research platform ScienceOne can empower the entire research process from "hypothesis formulation - plan formulation - simulation and analysis - experimental verification - law discovery", providing comprehensive intelligent support for basic research.
Official website report：https://www.cas.cn/cm/202511/t20251110_5087982.shtml
Summary
Overseas companies tend to make breakthrough innovations at the algorithmic level.
DeepMind has completely revolutionized the field of protein structure prediction through the AlphaFold series. SandboxAQ integrates quantum computing and AI deeply to break through the computational challenges of molecular systems. Google AI has achieved innovation in global weather forecasting by using graph neural networks. The core logic lies in first pushing the algorithmic capabilities to the limit, verifying the feasibility of the technical path, and then gradually expanding and extending it to specific application scenarios. This development model requires extremely solid basic research capabilities and continuous long-term investment. Once a breakthrough is achieved, the resulting technical barriers will possess a significant leading edge.
Domestic companies, on the other hand, place greater emphasis on the deep integration of algorithms, data and computing power, as well as the application in specific scenarios.
Baidu has deeply integrated its algorithms with the bio-computing platform to provide a solution covering the entire process; Tencent has integrated the drug discovery algorithms with the single-cell database to form a closed-loop service; DeepForce Technology has directly constructed a scientific research cloud platform serving multiple industries. These approaches are all carried out based on the actual needs of the research teams, aiming to efficiently convert AI capabilities into usable tools and services. This approach is more practical and easier to achieve commercial results in the short term.
The two paths are not superior or inferior; they are merely different in terms of choice.
It is clear, however, that comprehensive companies are gradually becoming indispensable infrastructure builders in the development of AI for Science. Their ability to break through disciplinary boundaries and integrate technological chains directly influences the extent to which AI research tools can penetrate into various scenarios and reach.
