- Leveraging Artificial Intelligence and Machine Learning to Advance Vital Drug Discovery and Development.
- Collaborations with Leading Research Institutions Including Johns Hopkins Applied Physics Laboratory.
- Annual Letter to Shareholders Issued, Covering 2025 Progress Points with Commercial Priorities and Potential Catalysts Detailed for 2026.
- Whitepaper Published on AI in Bioinformatics: Turning Complex Data into Actionable Insights.
- Publication Details How BullFrog Data Networks® and bfLEAP® Technology Address Fundamental AI Challenges in Drug Discovery and Development.
- Sales and Marketing Team Expanded to Accelerate Commercial Adoption of Clinical Trial Optimization Solutions and BFRG Technology.
- Expanded Sales Team Working to Accelerate Adoption of bfPREP Product and the Company’s Broader Suite of Trial Optimization Solutions.
BullFrog AI Holdings, Inc: (Nasdaq: BFRG) leverages Artificial Intelligence and machine learning to advance drug discovery and development. Through collaborations with leading research institutions, BFRG uses causal AI in combination with its proprietary bfLEAP® platform to analyze complex biological data, aiming to streamline therapeutics development and reduce failure rates in clinical trials.
Annual Letter to Shareholders Highlights Significant Progress in 2025 and Outlines Commercial Priorities and Potential Catalysts Entering 2026
On December 30th BFRG released its annual letter to shareholders, which can be reviewed in its entirety at this direct link: https://www.globenewswire.com/news-release/2025/12/30/3211355/0/en/BullFrog-AI-Publishes-Annual-Letter-to-Shareholders.html
The 2025 BFRG letter included the following points:
This year marked an important evolution for BullFrog AI, from platform development and early validation to commercialization and enterprise adoption. At the core of the BRFG approach is the belief that artificial intelligence should augment human expertise, not replace it, enabling scientists and clinicians to make better, faster, and more confident decisions across the drug discovery and development lifecycle through causal, explainable AI.
Commercial Traction and Expanding Reach
BFRG is currently engaged in multiple, advanced conversations with global pharmaceutical organizations, relating to potential commercial transactions ranging from out-licensing to discovery research collaborations. Each of these potential relationships represents a meaningful “shot on goal,” with deal structures that may include upfront and milestone payments, plus long-term revenue sharing.
With respect to 2025, during the second quarter, BFRG entered into a commercial collaboration with Sygnature Discovery, a leading global contract research organization, that is expected to generate significant revenue through 2028. Following the formal sales kickoff in late September, Sygnature’s global business development team is now actively introducing BFRG Data Networks™ to its biopharma client base, expanding our commercial reach.
In parallel, BFRG expanded internal sales and marketing efforts to directly support clinical trial optimization solutions. The team is increasingly leveraging demonstrable platform successes, including validated oncology and neuroscience use cases, to drive deeper engagement across prospects in oncology, neuroscience, and rare disease, creating a reinforcing cycle in which proof of impact supports broader adoption discussions.
Enterprise-Grade Platform Designed to Support Scaled Adoption
A major achievement this year was the continued evolution of BFRG technology into a modular, enterprise-grade solutions stack designed to support broader commercial adoption. The launch of BFRG Data Networks™ and the accompanying solutions library represents a meaningful advancement in how large biopharma organizations can deploy our technology within existing infrastructure and workflows.
Importantly, this architecture is designed to align with how enterprise customers buy and deploy technology, supporting recurring, scalable commercial relationships while maintaining the explainability and analytical rigor required for high-stakes scientific decision-making. As a result, the BFRG platform meets global pharmaceutical companies where their data resides and provides a foundation that can deepen in value as adoption expands across programs and use cases.
Scientific Validation with Real-World Impact
Equally significant, collaboration with Eleison Pharmaceuticals represents our first, substantive real-world deployment of the BFRG AI platforms within a late-stage oncology development program. This engagement demonstrated BFRG ability to operate within the rigor, scale, and constraints of advanced clinical development, delivering actionable insights in an environment where data quality, interpretability, and confidence are critical.
The project was highly successful from both a scientific and operational perspective. Based on the value delivered, Eleison is exploring an additional follow-on collaboration with BFRG, reinforcing belief that BFRG AI can serve as a long-term, trusted partner for companies advancing late-stage pharmaceutical assets.
Separately, insights generated through this collaboration were showcased in a jointly authored abstract that was accepted for presentation at the 2026 ASCO Gastrointestinal Cancers Symposium, a highly respected oncology forum.
bfPREP™: Turning a Bottleneck into an Opportunity
One of the most strategically important developments of the year was the launch of bfPREP™, the BFRG AI-powered data preparation and standardization module. Originally developed to support work with Eleison, bfPREP™ addresses a persistent and costly challenge in life sciences and virtually all industries: transforming messy, fragmented, unstructured data into structured AI-ready datasets.
Capital Discipline Designed for Asymmetric Upside
Throughout the year, BFRG maintained a disciplined approach to capital allocation while continuing to invest selectively in areas that support commercialization and platform scalability. BFRG AI operates with a lean cost structure, with an annual burn of approximately $6.2 million, which creates substantial operating leverage as commercial opportunities convert. As a result, even a single, meaningful partnership or revenue-generating engagement has the potential to materially improve the BFRG financial profile and extend its operating runway, providing flexibility to reinvest in growth while limiting dilution. BFRG has also put in place additional financing flexibility, including a $10 million equity line of credit and an ATM to support execution as opportunities
Whitepaper Published on AI in Bioinformatics
On November 18thBFRG announced the publication of a new whitepaper titled “AI in Bioinformatics: Turning Complex Data into Actionable Insights with BullFrog Data Networks®.”
The BFRG whitepaper identifies three major pitfalls in modern bioinformatics: the compositional data trap, the mirage of feature importance, and the overreach of generative AI. Each contributes to the high failure rate in clinical development and the waste of billions in R&D investments annually.
Ther BFRG bfLEAP® platform overcomes these challenges through causal inference modeling and probabilistic validation methods originally developed at the Johns Hopkins Applied Physics Laboratory. When integrated into BFRG Data Networks®, these capabilities enable reproducible insights across multimodal datasets—including genomics, transcriptomics, proteomics, and clinical data—creating a foundation for more reliable target identification, biomarker discovery, and patient stratification.
The publication reinforces the BFRG expanding role in solving one of the most pressing problems in drug development: the disconnect between massive data availability and actionable biological understanding. BFRG believes its modular, scalable approach—combining bfPREP™, bfLEAP®, and BFRG Data Networks®—is well positioned to become a cornerstone of AI-enabled R&D infrastructure for global pharmaceutical and biotech organizations.
The full BFRG whitepaper may be accessed at this direct link: https://bullfrogai.com/resources/ai-in-bioinformatics-turning-complex-datainto-actionable-insights-with-bullfrog-datanetworks/.
Sales and Marketing Team Expanded to Accelerate Commercial Adoption of Clinical Trial Optimization Solutions and bfPREP™ Module
On September 30th BFRG announced the expansion of its internal sales organization to support accelerated commercialization of the Company’s clinical trial optimization offerings, including its recently launched bfPREP™ module.
The expansion of BFRG in-house sales capabilities complements the Company’s recent commercial rollout with Sygnature Discovery, which is focused on selling BFRG Data Networks™ target selection solutions to global biopharma clients. By strengthening its direct sales organization, BFRG is positioned to accelerate adoption of its trial-focused platforms, including bfPREP, among pharmaceutical and biotechnology companies.
The BFRG bfPREP module, originally developed to support a collaboration with Eleison Pharmaceuticals, is purpose-built to handle the scale and complexity of diverse clinical trial data. From legacy records and siloed datasets to unstructured documents, bfPREP transforms raw information into standardized, analyzable formats at scale with results achievable in a matter of days.
The BFRG expanded sales team is working to accelerate adoption of bfPREP, and the Company’s broader suite of trial optimization solutions. Once data has been standardized through bfPREP, platforms such as bfLEAP® can be applied to uncover patient subgroups, inform trial design, and support more precise execution. By aligning these capabilities under a focused commercial strategy, BullFrog AI offers sponsors an end-to-end approach to improving trial efficiency and outcomes.
For more information visit BFRG visit: https://bullfrogai.com
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Company Name: BullFrog AI Holdings, Inc: (Nasdaq: BFRG)
Contact Person: Vin Singh, CEO
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Phone: (240) 658-6710
Address:325 Ellington Boulevard Unit 317
City: Gaithersburg
State: MD 20878
Country: United States
Website: https://bullfrogai.com
