AI-powered drug discovery is no longer a speculative bet,it's becoming a core holding for major investment indices and institutional investors. Insilico Medicine's recent inclusion in the Hong Kong Exchanges and Clearing (HKEX) Tech 100 Index marks a turning point, signaling that the market views generative AI in pharma as a mature, investable sector rather than an experimental frontier.

The HKEX Tech 100 Index, which tracks 100 of Hong Kong's largest technology companies with exposure to emerging tech trends, expanded its methodology in August 2026 to explicitly include "opportunities across the AI value chain." Insilico's addition, effective September 14, 2026, reflects the index's recognition that AI drug discovery has moved beyond proof-of-concept into commercial viability.

What's Driving the Shift From Hype to Institutional Confidence?

Insilico's financial performance provides concrete evidence of the sector's maturation. In the first half of 2026, the company reported total revenue of approximately $106 million, a 287 percent year-over-year increase, and achieved its first profitable half-year since listing on the Hong Kong Stock Exchange in December 2025. This profitability milestone came from a series of out-licensing, co-development, and research and development collaborations with global pharmaceutical partners.

The scale of these partnerships underscores institutional confidence in AI-driven drug discovery. As of late August 2026, Insilico announced total contract values reaching approximately $7.3 billion for the year alone, bringing cumulative partnership value since 2021 to approximately $11 billion. These aren't small pilot programs; they're major commitments from established pharmaceutical companies including Eli Lilly, Servier, Takeda, SK Biopharmaceuticals, and Qilu Pharmaceutical.

On the pipeline front, Insilico nominated nine development candidates within nine months of 2026, setting a new company record for annual productivity. The company also achieved eight clinical milestones across proprietary and co-developed programs. Leading this progress is Rentosertib (ISM001-055), which holds a significant distinction: it is the world's first drug candidate discovered and developed using generative AI to advance to Phase III clinical trials, where it is being evaluated for idiopathic pulmonary fibrosis, a serious lung disease with limited treatment options.

How Are Pharmaceutical Companies Using Causal AI to Tackle Complex Diseases?

Beyond traditional generative AI for molecular design, a new wave of AI technology is emerging to address disease complexity. Ono Pharmaceutical, a Japan-based company with a strong track record in neurology, recently partnered with Aitia, a US-based biotech firm, to apply causal AI to neurological disease discovery.

The partnership illustrates a critical shift in how AI is being applied to drug discovery. Rather than using correlation-based machine learning, which identifies statistical patterns, Aitia's REFS technology employs causal AI to build what the company calls Gemini Digital Twins (GDTs) of neurological diseases. These virtual patient models aim to mirror actual disease characteristics and identify causal relationships that conventional AI cannot uncover.

This distinction matters because neurological diseases involve extraordinarily complex mechanisms. Disease development depends on genetic factors, developmental processes, neural network dysfunction, immune system involvement, and environmental triggers all interacting in ways that standard correlation-based AI struggles to disentangle. Aitia will analyze clinical and omics data to construct these digital models and predict new therapeutic targets, while Ono Pharmaceutical secures exclusive worldwide rights to research, develop, and commercialize drug candidates targeting those identified pathways.

"Through this collaboration, we are delighted to leverage cutting-edge REFS technology to identify true therapeutic targets in neurological diseases, where disease mechanisms are complex and not yet fully understood. We believe this will accelerate the development of innovative medicines," said Seishi Katsumata, Corporate Officer and Executive Vice President of Discovery and Research at Ono.

Seishi Katsumata, Corporate Officer and Executive Vice President of Discovery and Research at Ono Pharmaceutical

Steps to Understanding the AI Drug Discovery Investment Landscape

• Index Inclusion as a Maturity Signal: When major stock indices add AI pharma companies to their benchmarks, it signals that institutional investors view the sector as stable enough for passive index-tracking funds and long-term capital allocation, not just venture capital speculation.
• Partnership Scale as a Validation Metric: Multi-billion-dollar collaborations with established pharmaceutical giants like Eli Lilly and Takeda indicate that these companies are willing to bet significant capital and pipeline resources on AI-discovered candidates, moving beyond small research grants.
• Clinical Advancement as Proof of Concept: The progression of AI-discovered candidates like Rentosertib into Phase III trials demonstrates that AI-generated drug candidates can survive rigorous preclinical and early clinical scrutiny, addressing long-standing skepticism about AI's ability to produce viable therapeutics.

The convergence of these signals,index inclusion, record profitability, multi-billion-dollar partnerships, and clinical advancement,suggests that AI drug discovery has transitioned from a speculative technology to an established component of the pharmaceutical innovation ecosystem. Institutional investors are now treating AI-powered drug discovery as a core holding rather than a high-risk bet, fundamentally reshaping how capital flows into biotech research and development.

"We are honored to partner with Ono, a company with a strong track record in the neurology field. By utilizing our state-of-the-art causal AI-powered REFS technology, we are confident that we can build Gemini Digital Twins that uncover true disease mechanisms and therapeutic targets that could not be identified with conventional AI, thereby accelerating the development of new therapies," said Colin Hill, CEO and co-founder of Aitia.

Colin Hill, CEO and co-founder of Aitia

For investors, researchers, and pharmaceutical executives, this shift has practical implications. The inclusion of AI pharma companies in mainstream indices means these stocks are now accessible through standard index funds and exchange-traded funds (ETFs), lowering barriers to investment. For drug developers, the success of partnerships like Ono and Aitia's suggests that combining different AI approaches,generative AI for molecular design and causal AI for target discovery,may unlock therapeutic opportunities that neither approach could achieve alone.