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Bullfrog AI Holdings, Inc. Plans $8 Million IPO for Week of December 12th (BFRG)

Bullfrog AI Holdings, Inc. Plans $8 Million IPO for Week of December 12th (BFRG)

Bullfrog AI Holdings, Inc. 計劃在12月12日當週進行800萬美元的首次公開募股(BFRG)
Financial News Live ·  2022/12/08 22:02

Bullfrog AI Holdings, Inc. (BFRG) is planning to raise $8 million in an IPO on the week of December 12th, IPO Scoop reports. The company will issue 1,300,000 shares at $6.38 per share.

IPO Scoop報道,Bullfrog AI Holdings, Inc.(BFRG)計劃在12月12日當週通過首次公開募股籌集800萬美元。該公司將以每股6.38美元的價格發行13萬股股票。

WallachBeth Capital and ViewTrade Securities served as the underwriters for the IPO.

WallachBeth Capital和ViewTrade Securities是首次公開募股的承銷商。

Bullfrog AI Holdings, Inc. provided the following description of their company for its IPO: "(Note: This is a unit IPO of 1.32 million units (1,317,647 units) at $6.375 per unit. 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 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.) 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: ● acquire the rights to a drug from a biopharmaceutical industry company or academia, ● use the proprietary bfLEAP AI/ML platform to determine a multi-factorial profile for a patient that would best respond to the drug, ● rapidly conduct a clinical trial to validate the drug's use for the defined "high-responder" population, and ● divest/sell the rescued drug asset with the new information back to a large player in the pharma industry, following positive results of the clinical trial. **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. ".

Bullfrog AI Holdings, Inc. 對其公司的首次公開募股提供了以下描述:“(注意:這是132萬套(1317,647單位)的單位首次公開募股,每單位6.375美元。每個單位由一股普通股和一份購買一股股票的可交易認股權證組成。Bullfrog AI Holdings, Inc. 於2022年12月8日提交了一份S-1/A,其中披露了其股票及其在納斯達克交易的認股權證的新擬議符號——股票爲BRFG,認股權證爲BRGW——並更新了截至2022年9月30日的財務報表。該公司於2022年10月19日提交了S-1,並披露了其單位首次公開募股的條款——132萬套(1317,647美元,每單位6.375美元),以籌集840萬美元。Bullfrog AI 控股公司於 2022 年 6 月 10 日提交了機密的 IPO 文件。在IPO結束之前,將進行1比7的反向股票拆分。)我們使用人工智能和機器學習爲內部和外部項目開發藥物。我們致力於提高成功概率,減少開發療法所花費的時間和成本。當前的大多數人工智能/機器學習平臺在合成不同的高維數據以獲得切實可行的見解方面仍然不足。我們的平臺技術名爲 bfLeap,是一種分析性人工智能/機器學習平臺,該平臺源自約翰·霍普金斯大學應用物理實驗室 (JHU-APL) 開發的技術,通過對研究人員和臨牀醫生的數據提供更精確的多維理解,克服目前阻礙研究人員和臨牀醫生的可擴展性和靈活性挑戰。我們正在部署 bfLeap,用於內部項目開發的幾個關鍵階段,並通過戰略伙伴關係和合作來簡化療法開發中的數據分析,通過降低新療法的失敗率來降低總體開發成本,並影響無數患者的生活,否則他們可能無法獲得所需療法。BfLeap 平臺利用監督和無監督的機器學習——因此,它無需事先假設即可揭示數據中的真實/有意義的聯繫。監督式機器學習使用帶標籤的輸入和輸出數據,而無監督學習算法不使用。在監督學習中,算法通過對數據進行迭代預測並調整正確答案來從訓練數據集 “學習”。無監督學習,也稱爲無監督機器學習,使用機器學習算法來分析和聚類未標記的數據集。這些算法無需人工干預即可發現隱藏的模式或數據組。bfLeap 平臺中使用的算法旨在處理高度不平衡的數據集,以成功識別與感興趣結果相關的因素組合。與我們的戰略合作伙伴和合作夥伴一起,我們的主要目標是提高臨牀前和臨牀療法開發的任何階段的成功幾率。我們的主要商業模式是提高藥物開發的成功率和效率,這可以通過收購藥物或與正在開發藥物的公司建立夥伴關係和合作來實現。我們希望通過戰略收購當前臨牀階段和失敗藥物進行內部開發,或者通過與生物製藥行業公司的戰略合作伙伴關係來實現這一目標。我們能夠利用強大且久經考驗的人工智能/機器學習平臺(商品名:BfLeap)來開展我們的藥物資產增強業務,該平臺最初源自JHU-APL開發的技術。我們認爲,bfLeap 分析平臺是分析臨牀前和/或臨牀數據集的潛在顛覆性工具,例如在轉化研發和臨牀試驗環境中生成的強大臨牀前和臨牀試驗數據集。2021 年 11 月,我們修改了與 JHU-APL 的協議,加入了更多先進的人工智能技術。我們的平臺與疾病適應症或治療方式無關,因此我們認爲它在生物製劑或小分子的開發中具有價值。我們的藥物資產增強計劃的流程是:● 從生物製藥行業公司或學術界獲得藥物的權利,● 使用專有的 bfLeap AI/ML 平臺爲患者確定對藥物產生最佳反應的多因素特徵,● 快速進行臨牀試驗,以驗證該藥物在定義的 “高反應者” 人羣中的用途,並且 ● 使用新信息剝離/出售獲救的藥物資產在臨牀試驗取得積極結果之後,又回到了製藥行業的大型企業。**注意:招股說明書稱,在截至2022年9月30日的九個月中,該公司的運營淨虧損約爲211萬美元(合2106,969美元),沒有收入。”

Bullfrog AI Holdings, Inc. was founded in 2020 and has 4 employees. The company is located at 323 Ellington Blvd, Unit 317 Gaithersburg, MD 20878 and can be reached via phone at (240) 658-6710.

Bullfrog AI Holdings, Inc. 成立於 2020 年,有 4 名員工。該公司位於馬里蘭州蓋瑟斯堡市埃靈頓大道323號,317號單元20878,可通過電話 (240) 658-6710 與該公司聯繫。

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