At the 17th Annual Meeting of the New Champions (Summer Davos 2026), organized by the World Economic Forum in Dalian on June 23, Gong Yingying, Founder and Chairwoman of Yidu Tech, joined a panel titled “Faster Drugs, Better Access?” to discuss the evolving landscape of pharmaceutical innovation. Her remarks focused on the transformative role of artificial intelligence and healthcare data infrastructure in accelerating drug discovery and improving clinical outcomes worldwide.

The discussion, moderated by Li Xin, Managing Editor of Caixin Global, also included Giovanni Caforio, Chairman of Novartis, Ren Minghui, Professor at Peking University’s School of Public Health, and Eric Tse S. Y., Chief Executive Officer of SBP Group.

Building the Foundation for AI-Driven Healthcare
China has emerged as the second-largest market for innovative drug development, with clinical research activity growing rapidly. Despite this momentum, the industry continues to face obstacles including fragmented data systems, lengthy research cycles, recruitment challenges for clinical trials, and difficulties in generating reliable evidence that supports broader patient access to new treatments.

Gong emphasized that these issues extend beyond operational inefficiencies and are rooted in the fragmented nature of healthcare data itself. She explained that data often exists in isolated systems, lacks consistency, and remains disconnected across institutions, limiting the ability of AI technologies to deliver meaningful impact.

To address this challenge, Yidu Tech has focused on developing healthcare data infrastructure as a core strategic priority.

According to Gong, an effective medical AI ecosystem consists of four key layers: computing power, foundation models, healthcare-specific foundation models, and a robust high-quality data layer. She described this final layer as an “evidence foundation,” where raw healthcare data is transformed through standardization, governance, and validation into structured, traceable, and clinically reliable information.

With this evidence-based framework in place, healthcare providers can deploy AI systems that are safe, transparent, and compliant with regulatory requirements. Gong noted that such infrastructure has become essential for improving both the speed and quality of pharmaceutical research.

A central component of this strategy is Yidu Tech’s proprietary AI platform, YiduCore. The system converts fragmented healthcare information into structured medical knowledge and research intelligence. By September 30, 2025, YiduCore had processed nearly seven billion authorized medical records, connected over 10,000 healthcare institutions, and established a disease knowledge graph encompassing virtually every known disease.

Enhancing Drug Development Through Data Infrastructure
Drawing on partnerships with leading hospitals and research organizations throughout China, Yidu Tech has expanded its data infrastructure capabilities beyond clinical settings to support the entire drug development lifecycle.

During the early stages of research, the company’s disease knowledge graph assists scientists in identifying promising therapeutic areas, evaluating study feasibility, and designing more effective clinical trial protocols. These capabilities help reduce costly missteps, improve resource allocation, and increase the likelihood of successful outcomes.

As clinical trials move into execution, the platform enables intelligent patient screening and matching across multiple locations and research sites. This approach improves recruitment efficiency, shortens enrollment timelines, and helps address common issues such as low matching accuracy and limited patient diversity.

The platform also supports AI-powered quality assurance, continuous data verification, and proactive monitoring of adverse events throughout the trial process. These features strengthen compliance, improve data integrity, and reduce operational risks.

Following drug development, Yidu Tech’s standardized evidence framework facilitates the organization of real-world data, generation of clinical evidence, and analysis of patient populations. These capabilities support post-market research, expansion into additional treatment indications, reimbursement evaluations, and broader clinical adoption, creating a continuous value chain from innovation to patient access.

Gong highlighted that while clinical trial optimization traditionally depended heavily on human expertise, the combination of standardized healthcare data and AI technologies has now become a fundamental requirement for conducting high-quality research and accelerating pharmaceutical innovation.

Expanding Medical AI Internationally While Respecting Data Sovereignty
Discussing international cooperation and healthcare data governance, Gong stressed the importance of prioritizing security, privacy, and regulatory compliance.

She noted that healthcare information is typically stored within sovereign cloud environments governed by national regulations. Independent organizations oversee data protection, access control, auditing, and compliance to ensure information remains secure and properly managed.

Gong also pointed out that healthcare differs significantly from industries that can deploy standardized global products. Medical systems are shaped by local regulations, healthcare policies, and population needs, making localization a critical factor for successful international expansion.

As an example, she cited Yidu Tech’s work in Brunei. Through a joint venture established with the Bruneian government and supported by a shared technical team, the company co-operates the country’s national digital health platform, BruHealth. The collaboration allows Yidu Tech to introduce proven AI healthcare capabilities while fully adhering to local requirements regarding data sovereignty and privacy.

Concluding the discussion, Gong reaffirmed that Yidu Tech’s continued investment in healthcare AI infrastructure is guided by a clear objective: ensuring that technological innovation ultimately delivers meaningful benefits to patients. She emphasized that technology itself is not the end goal; rather, it serves as a means to create healthcare systems that are more efficient, reliable, and accessible for everyone.

Basecamp Research, a cutting-edge AI lab focused on biological design, has unveiled the Trillion Gene Atlas—an ambitious scientific program aimed at generating and modeling genomic data at an unprecedented trillion-gene scale. Developed in collaboration with Anthropic, Ultima Genomics, and PacBio, and supported by NVIDIA’s AI infrastructure, the initiative seeks to increase known genetic diversity by 100 times. It plans to gather genomic information from over 100 million species across thousands of global locations.

This effort builds on Basecamp Research’s expanding network of biodiversity partners worldwide. The long-term vision is to create a vast and diverse dataset that allows AI systems to learn from evolution and enable the on-demand design of new medicines.

Speaking at SXSW in Austin, Co-founder and CEO Glen Gowers noted that current biological AI models rely on a limited representation of Earth’s biodiversity. He explained that the Trillion Gene Atlas will dramatically expand the genetic landscape available for analysis, introducing a new era of programmable therapeutic design powered by large-scale data.

Comparable in scope to the Human Genome Project, the initiative was introduced during SXSW’s Health Track and at the NVIDIA GTC conference in San Jose.

Tackling the Biological Data Gap
Despite rapid growth in model size and computational capabilities, progress in AI-driven drug discovery has been constrained by limited data diversity. Most existing sequence-based models depend heavily on a small set of public databases, with a large portion trained on fewer than 250 million genetic sequences.

To address this, Basecamp Research introduced its EDEN foundation models earlier this year. These models are trained entirely on BaseData™, a proprietary genomic dataset that exceeds the size of all public repositories combined. By incorporating over 10 billion previously unknown genes from one million newly identified species, EDEN has revealed new scaling principles for AI in biology.

This expansion has enabled EDEN to move beyond prediction, allowing it to design therapeutics directly from disease prompts. In laboratory tests, the model demonstrated zero-shot functionality in human T-cells without relying on clinical or human-derived data. It has also produced promising results across multiple advanced applications, including AI-driven gene insertion and the creation of targeted antimicrobial peptides with high success rates.

The Trillion Gene Atlas builds on this foundation by significantly increasing both the scale and contextual richness of genomic data available for AI training.

Expanding a Global Biodiversity Network
Over the past six years, Basecamp Research has established a network of scientific collaborators spanning 31 countries. This has enabled the development of a scalable genomics pipeline designed specifically for AI applications. Using innovative regulatory frameworks and off-grid DNA sequencing technologies, the company is able to collect high-quality genetic data from remote ecosystems often inaccessible to traditional labs.

These partnerships emphasize knowledge sharing, local capacity building, and fair access and benefit-sharing agreements aligned with emerging global standards. As part of the Atlas initiative, new collaborations have been announced in Chile and Argentina, along with expanded research efforts in Antarctica.

Advancing Sequencing and Computing Capabilities
The Trillion Gene Atlas is made possible by breakthroughs in high-throughput sequencing and accelerated computing. Partnerships with Ultima Genomics and PacBio enable large-scale sequencing, including highly accurate long-read data that preserves detailed genomic context.
Ultima’s latest sequencing platform, the UG200 Series, is designed for industrial-scale genome and multi-omics sequencing at lower costs, making projects like the Atlas feasible. Meanwhile, PacBio’s HiFi sequencing technology provides precise, information-rich data critical for training advanced biological AI systems.

NVIDIA’s computing infrastructure will power the processing of massive genomic datasets at the petabase level. By leveraging tools like NVIDIA Parabricks, Basecamp aims to dramatically accelerate metagenomic analysis. Tasks that previously could have taken over two decades are now expected to be completed in under two years through parallel processing, automation, and large-scale model training.

Toward End-to-End AI-Driven Therapeutic Design
Anthropic is contributing to the initiative by integrating its AI system, Claude, with scientific platforms. The goal is to combine Claude’s reasoning capabilities with EDEN’s therapeutic design functions and NVIDIA’s data processing tools to create a seamless workflow—from interpreting complex biological data to generating targeted treatments.

Built on three core pillars—large-scale DNA sequencing, global data partnerships, and advanced computing—the Trillion Gene Atlas represents a major step toward transforming how biological data is used. By expanding evolutionary datasets 100-fold, Basecamp Research aims to accelerate drug discovery, improve precision in therapeutic design, and extend advances in areas such as gene therapy and antimicrobial resistance.