AI now generates molecules far faster than the wet lab can test them.

The bottleneck in #AIxBio has shifted. That’s why we've built one of the world’s largest self-driving lab fleets: 300+ workstations running 24/7, generating data 40x more efficiently.

The next gains in AI drug discovery will come from tighter design-to-experiment loops, not bigger models alone.

Our new collaboration with @PierreFabre puts this into action: de novo small-molecule design, tightly coupled with robotic lab feedback in multiple #Oncology programs.

Full announcement:
en.xtalpi.com/laboratoires-p…

The focus is difficult-to-drug targets, with scope to expand into dermatology and rare diseases. XtalPi will design and deliver molecules using its AI- and robotics-driven platform; Pierre Fabre will advance them. Terms include upfront, milestones, and downstream participation.

AI now generates molecules far faster than the wet lab can test them.

The bottleneck in #AIxBio has shifted. That’s why we've built one of the world’s largest self-driving lab fleets: 300+ workstations running 24/7, generating data 40x more efficiently.

AI designs move through synthesis and validation, with successes and failures fed back into our models to improve the next designs.

We’re building toward a future where experimental capacity keeps pace with AI’s ability to design.

That means more scope to pursue new therapeutic approaches and advance them into development. For us, that’s how #AIforScience changes the future of medicine.

XtalPi joins Tencent, Alibaba and SMIC in the top ten by weight of #HKEX’s new Artificial Intelligence Index.

The 40-company benchmark places #AIforScience, including drug discovery and biotechnology, alongside chips, infrastructure and foundation models.

The index goes beyond market capitalization. HKEX also factors R&D intensity, capital expenditure and revenue growth into constituent selection and weighting. Companies must derive at least 30% of revenue from eligible AI activities.

Inclusion in HKEX’s AI Index recognizes years of #AI and #robotics work in drug and materials discovery. More ahead.

Inclusion in HKEX’s AI Index recognizes years of #AI and #robotics work in drug and materials discovery. More ahead.

The index goes beyond market capitalization. HKEX also factors R&D intensity, capital expenditure and revenue growth into constituent selection and weighting. Companies must derive at least 30% of revenue from eligible AI activities.

Pipeline update: proprietary and partnered programs both advancing 🚀

KQTD-126 (our proprietary gut-restricted pan-TRK inhibitor) just cleared FDA IND for chronic intestinal pain. SIGX1094 (with Signet) got NMPA approval for a Ph. II/III combination trial in KRAS-G12C lung esigned to treat chronic intestinal pain associated with IBS and IBD while limiting systemic exposure. Preclinical studies showed sub-nanomolar pan-TRK inhibition and a gut-to-blood exposure ratio exceeding 1,000:1. The program was optimized through our AI + robotics platform to balance potency, selectivity, and tissue restriction.

SIGX1094 is a FAK/SRC dual inhibitor co-developed with Signet Therapeutics. The newly approved Phase II/III trial combines SIGX1094 with Innovent’s KRAS-G12C inhibitor fulzerasib in KRAS-G12C-mutant non-small cell lung cancer, including previously untreated patients and an exploratory cohort with resistance to prior KRAS-G12C inhibitor treatment. The trial expands SIGX1094’s clinical development beyond diffuse gastric cancer, marking another clinical application of our AI-and-organoid approach to solid-tumor drug discovery.

1/ What happens when regional medicinal knowledge meets AI-powered drug discovery? XtalPi and #DubaiMedicalUniversity are exploring that intersection in a new partnership.

#TraditionalMedicines are a key area of shared interest, alongside broader pharmaceutical research.

3/ A glimpse of what’s possible: our fully automated herbal medicine lab built with Hengqin Laboratory has been operational for some time. Showcased at #WHXDubai in Feb, it integrates AI and robotics for extraction, purification, compound identification, and bioactivity testing in one workflow.

4/ Our relationship with DMU deepens our ties with the #UAE’s scientific community and creates new opportunities for research with global impact. We’re proud to support the country’s vision to become a global hub for life sciences and pharmaceutical innovation.

Introducing Hydrogen-1.0, Deep Principle’s first general-purpose large AI model, built to support scientific discovery.

Hydrogen-1.0 helps researchers reason through complex scientific questions, carry out calculations and simulations, turn research ideas into experimental to guide further exploration.

Today, we’re releasing its model weights for researchers and developers to adapt and evaluate on their own scientific tasks. Hydrogen-1.0 is also available in our AI Scientist product, MIRA.

Explore the benchmarks, research examples, and ways to get started in the posts below.

Building intelligence for scientific discovery.

From medicines to materials, XtalPi’s approach centers on one principle: connect AI with automation to close the loop between hypothesis and experimental validation.

At our SWITCH event, XtalPi Co-founder and Chief Innovation Officer Dr. Lipeng Lai presented the company’s across industries and modalities—from small molecules, peptides and siRNA to materials discovery and AI virtual cell models.

Data informs models. Models guide experiments. Automated experiments generate new evidence to refine the models and test the next hypothesis. This continuous cycle is at the core of XtalPi’s approach.

When historical data is limited in quality and quantity, progress depends on how quickly researchers can generate reliable evidence. Integrating AI, robotics and scientific expertise creates the infrastructure to test ideas, learn from outcomes and deepen our understanding of complex biology.

Realizing that ambition takes a community. XtalPi is building an open science platform and ecosystem that brings together complementary expertise, tools and technologies—enabling more researchers and industries to participate in AI-driven discovery.

#SWITCH2026 #XtalPi #AIforScience #DrugDiscovery #MaterialsScience #Robotics