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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. 计划在12月12日当周进行800万美元的首次公开募股(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报道,Bullfrog AI Holdings, Inc.(BFRG)计划在12月12日当周通过首次公开募股筹集800万美元。该公司将以每股6.38美元的价格发行13万股股票。

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

WallachBeth Capital和ViewTrade Securities是首次公开募股的承销商。

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. 对其公司的首次公开募股提供了以下描述:“(注意:这是132万套(1317,647单位)的单位首次公开募股,每单位6.375美元。每个单位由一股普通股和一份购买一股股票的可交易认股权证组成。Bullfrog AI Holdings, Inc. 于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纳斯达克交易的认股权证的新拟议符号——股票为BRFG,认股权证为BRGW——并更新了截至2022年9月30日的财务报表。该公司于2022年10月19日提交了S-1,并披露了其单位首次公开募股的条款——132万套(1317,647美元,每单位6.375美元),以筹集840万美元。Bullfrog AI 控股公司于 2022 年 6 月 10 日提交了机密的 IPO 文件。在IPO结束之前,将进行1比7的反向股票拆分。)我们使用人工智能和机器学习为内部和外部项目开发药物。我们致力于提高成功概率,减少开发疗法所花费的时间和成本。当前的大多数人工智能/机器学习平台在合成不同的高维数据以获得切实可行的见解方面仍然不足。我们的平台技术名为 bfLeap,是一种分析性人工智能/机器学习平台,该平台源自约翰·霍普金斯大学应用物理实验室 (JHU-APL) 开发的技术,通过对研究人员和临床医生的数据提供更精确的多维理解,克服目前阻碍研究人员和临床医生的可扩展性和灵活性挑战。我们正在部署 bfLeap,用于内部项目开发的几个关键阶段,并通过战略伙伴关系和合作来简化疗法开发中的数据分析,通过降低新疗法的失败率来降低总体开发成本,并影响无数患者的生活,否则他们可能无法获得所需疗法。BfLeap 平台利用监督和无监督的机器学习——因此,它无需事先假设即可揭示数据中的真实/有意义的联系。监督式机器学习使用带标签的输入和输出数据,而无监督学习算法不使用。在监督学习中,算法通过对数据进行迭代预测并调整正确答案来从训练数据集 “学习”。无监督学习,也称为无监督机器学习,使用机器学习算法来分析和聚类未标记的数据集。这些算法无需人工干预即可发现隐藏的模式或数据组。bfLeap 平台中使用的算法旨在处理高度不平衡的数据集,以成功识别与感兴趣结果相关的因素组合。与我们的战略合作伙伴和合作伙伴一起,我们的主要目标是提高临床前和临床疗法开发的任何阶段的成功几率。我们的主要商业模式是提高药物开发的成功率和效率,这可以通过收购药物或与正在开发药物的公司建立伙伴关系和合作来实现。我们希望通过战略收购当前临床阶段和失败药物进行内部开发,或者通过与生物制药行业公司的战略合作伙伴关系来实现这一目标。我们能够利用强大且久经考验的人工智能/机器学习平台(商品名:BfLeap)来开展我们的药物资产增强业务,该平台最初源自JHU-APL开发的技术。我们认为,bfLeap 分析平台是分析临床前和/或临床数据集的潜在颠覆性工具,例如在转化研发和临床试验环境中生成的强大临床前和临床试验数据集。2021 年 11 月,我们修改了与 JHU-APL 的协议,加入了更多先进的人工智能技术。我们的平台与疾病适应症或治疗方式无关,因此我们认为它在生物制剂或小分子的开发中具有价值。我们的药物资产增强计划的流程是:● 从生物制药行业公司或学术界获得药物的权利,● 使用专有的 bfLeap AI/ML 平台为患者确定对药物产生最佳反应的多因素特征,● 快速进行临床试验,以验证该药物在定义的 “高反应者” 人群中的用途,并且 ● 使用新信息剥离/出售获救的药物资产在临床试验取得积极结果之后,又回到了制药行业的大型企业。**注意:招股说明书称,在截至2022年9月30日的九个月中,该公司的运营净亏损约为211万美元(合2106,969美元),没有收入。”

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. 成立于 2020 年,有 4 名员工。该公司位于马里兰州盖瑟斯堡市埃灵顿大道323号,317号单元20878,可通过电话 (240) 658-6710 与该公司联系。

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