Vision & Mission

In recent years, rapid advancements in AI—particularly generative AI and large language models (LLMs)—have led to significant progress in fields such as natural language understanding and generation, computer vision, and speech processing. AI has shown an incredible ability to mimic human intelligence, motivating researchers to further develop AI to emulate natural intelligence. For example, building generative protein language models for drug discovery. In addition, the use of AI in life sciences has been fueled by the rapid digital transformation of biology and lab automation, providing big data for AI to learn through closed-loop feedback and benefit from scaling laws. The application of AI in designing new chemical compounds and materials also shows potential to disrupt traditional methods, creating a paradigm shift.

To facilitate the development of AI for science, we are committed to building a new type of research organization that brings together experts in computer science, machine learning, data science, statistics, and bioinformatics. Our goal is to foster interdisciplinary collaboration to develop AI-powered scientific discovery and technological innovation in fields such as biology, chemistry, material science, energy, and agriculture, with a significant impact on the quality of life. We will shape the future of science with our partners from industry, government, and academia, supported by the five pillars of our R&D:

An open AI for Science (AI4S) platform and infrastructure for developing and deploying AI4S.
Deeply integrated research of sciences and AI.
A global academic network & community.
An ecosystem of world-leading industry partners.
An incubator and venture capital network to facilitate industrial development and drive economic growth.

We aim to redefine science through a systematic effort to build and organize knowledge and foundation models for both AI and humans to collaborate in new ways, leveraging AI’s strengths, such as understanding and generating high-dimensional data and extracting underlying patterns, structures, and relationships from large volumes of data. We will advance both “AI as a Science” and “AI for Science,” creating revolutionary technologies with global impact.