We use artificial intelligence and machine learning to advance medicines for both internal and external projects. We are committed to increasing the probability of success and decreasing the time and cost involved in developing therapeutics. Most current AI/ML platforms still fall short in their ability to synthesize disparate, high-dimensional data for actionable insight. Our platform technology, named, bfLEAP™, is an analytical AI/ML platform derived from technology developed at The Johns Hopkins University Applied Physics Laboratory (JHU-APL), which is able to surmount the challenges of scalability and flexibility currently hindering researchers and clinicians by providing a more precise¹, multi-dimensional understanding of their data. We are deploying bfLEAP™ for use at several critical stages of development for internal programs and through strategic partnerships and collaborations with the intention of streamlining data analytics in therapeutics development, decreasing the overall development costs by decreasing failure rates for new therapeutics, and impacting the lives of countless patients that may otherwise not receive the therapies they need.

The bfLEAP™ platform utilizes both supervised and unsupervised machine learning – as such, it is able to reveal real/meaningful connections in the data without the need for a prior hypothesis. Supervised machine learning uses labeled input and output data, while an unsupervised learning algorithm does not. In supervised learning, the algorithm “learns” from the training dataset by iteratively making predictions on the data and adjusting for the correct answer. Unsupervised learning, also known as unsupervised machine learning, uses machine learning algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention. Algorithms used in the bfLEAP™ platform are designed to handle highly imbalanced data sets to successfully identify combinations of factors that are associated with outcomes of interest.

Together with our strategic partners and collaborators, our primary goal is to improve the odds of success at any stage of pre-clinical and clinical therapeutics development. Our primary business model is improving the success and efficiency of drug development which is accomplished either through acquisition of drugs or partnerships and collaborations with companies that are developing drugs. We hope to accomplish this through strategic acquisitions of current clinical stage and failed drugs for in-house development, or through strategic partnerships with biopharmaceutical industry companies. We are able to pursue our drug asset enhancement business by leveraging a powerful and proven AI/ML platform (trade name: bfLEAP™) initially derived from technology developed at JHU-APL. We believe the bfLEAP™ analytics platform is a potentially disruptive tool for analysis of pre-clinical and/or clinical data sets, such as the robust pre-clinical and clinical trial data sets being generated in translational R&D and clinical trial settings. In November 2021, we amended the agreement with JHU-APL to include additional advanced AI technology.

Our platform is agnostic to the disease indication or treatment modality and therefore we believe that it is of value in the development of biologics or small molecules.

The process for our drug asset enhancement program is to:

**Note: For the nine months that ended Sept. 30, 2022, the company had a net loss from operations of about $2.11 million ($2,106,969) and no revenues, the prospectus says.

(Note: Bullfrog AI Holdings, Inc. priced its unit IPO on Feb. 13, 2023, at $6.50 – the assumed IPO price in the prospectus – on slightly fewer units – 1.30 million units (1,297,318 units) – down from the 1.32 million units (1,317,647 units) in the prospectus – to raise $8.4 million, the company announced before the U.S. stock market’s open on Tuesday, Feb. 14, 2023 – aka Valentine’s Day 2023. The IPO raised $160,000 less – or 1.9 percent less – than if the full number of shares had been priced.)

(Note: Bullfrog AI Holdings, Inc., filed an S-1/A dated Jan. 10, 2023, in which it changed one of the joint book-runners for its unit IPO and raised the assumed IPO price. Kingswood Capital Markets replaced ViewTrade Securities as a joint book-runner – with WallachBeth Capital in the lead left position – according to the Jan. 10, 2023, SEC filing. The assumed offering price is now $6.50 – up from $6.375 previously – on the same number of units: 1.32 million units (1,317,647 units) – to raise $8.56 million. Each unit consists of one share of common stock and one tradeable warrant to buy one share of stock. Bullfrog AI Holdings, Inc. filed an S-1/A dated Dec. 8, 2022, in which it disclosed new proposed symbols for its stock and its warrants to trade on the NASDAQ – BRFG for the stock and BFRGW for the warrants – and updated its financial statements through Sept. 30, 2022. The company filed its S-1 on Oct. 19, 2022, and disclosed terms for its unit IPO – 1.32 million units (1,317,647 units) at $6.375 per unit – to raise $8.4 million. Bullfrog AI Holdings filed confidential IPO documents on June 10, 2022. A 1-for-7 reverse stock split will take place before the IPO closes.)