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Bullfrog AI Holdings, Inc. Prices IPO at $6.38 Per Share (BFRG)

Bullfrog AI Holdings, Inc. Prices IPO at $6.38 Per Share (BFRG)

Bullfrog AI Holdings, Inc. 将首次公开募股定价为每股6.38美元(BFRG)
Defense World ·  2022/12/30 14:21

Bullfrog AI Holdings, Inc. (BFRG) expects to raise $8 million in an initial public offering on Tuesday, January 3rd, IPO Scoop reports. The company plans to issue 1,300,000 shares at a price of $6.38 per share.

据IPO Scoop报道,牛蛙人工智能控股公司(BFRG)预计将于1月3日(星期二)首次公开募股(IPO),筹集800万美元。该公司计划以每股6.38美元的价格发行130万股。

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

WallachBeth Capital和ViewTrade Securities担任此次IPO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. ".

牛蛙人工智能控股公司在其首次公开募股中提供了以下对其公司的描述:“(注:这是一次132万个单位(1317647个单位)的单位首次公开募股,每单位6.375美元。每个单位包括一股普通股和一股购买一股股票的可交易认股权证。牛蛙AI控股有限公司提交了一份日期为2022年12月8日的S-1/A文件,其中披露了其股票和在纳斯达克交易的权证的新拟议代码-BRFG购买股票,BFRGW购买认股权证-并更新了截至9月1日的财务报表。30,2022年。该公司于2022年10月19日提交了S-1文件,并披露了132万个单位的首次公开募股条款(1317647个单位,每单位6.375美元),以筹集840万美元。牛蛙AI控股于2022年6月10日提交了保密的IPO文件。7股1股的反向股票拆分将在IPO结束前进行。)我们使用人工智能和机器学习来推进内部和外部项目的药物。我们致力于增加成功的可能性,并减少开发疗法所涉及的时间和成本。目前大多数AI/ML平台在综合不同的高维数据以获得可操作的洞察力方面仍然存在不足。我们的平台技术名为bfLEAP,是一个分析性AI/ML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,通过提供对他们的数据的更精确、多维的理解,能够克服目前阻碍研究人员和临床医生的可扩展性和灵活性方面的挑战。我们正在部署bfLEAP,用于内部项目开发的几个关键阶段,并通过战略合作伙伴关系和合作,旨在简化治疗开发中的数据分析, 通过降低新疗法的失败率来降低总体开发成本,并影响到无数原本可能得不到所需疗法的患者的生活。BfLEAP平台同时利用监督和非监督机器学习-因此,它能够揭示数据中真实/有意义的联系,而不需要事先假设。有监督的机器学习使用标记的输入和输出数据,而非监督学习算法不使用。在有监督学习中,该算法通过迭代地对数据进行预测并调整正确答案来从训练数据集中“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分组。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的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治疗发展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和效率,这是通过收购药物或与开发药物的公司建立伙伴关系和合作来实现的。我们希望通过战略性收购目前的临床阶段和失败的药物进行内部开发来实现这一目标, 或者通过与生物制药行业公司的战略合作伙伴关系。我们能够通过利用最初源自JHU-APL开发的技术的强大且经过验证的AI/ML平台(商标:bfLEAP)来开展我们的药物资产增强业务。我们相信,bfLEAP分析平台对于分析临床前和/或临床数据集是一个潜在的颠覆性工具,例如在翻译研发和临床试验环境中生成的强大的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,纳入了额外的先进人工智能技术。我们的平台与疾病适应症或治疗方式无关,因此我们相信它在生物制品或小分子的开发中是有价值的。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平台为患者确定对该药物最有效的多因素特征,●快速进行临床试验,以验证该药物在确定的“高响应者”人群中的用途,以及●根据临床试验的积极结果,剥离/出售带有新信息的挽救的药物资产给制药行业的大型参与者。*注:截至9月9日的9个月招股说明书说,2022年30日,该公司运营净亏损约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.

牛蛙AI控股有限公司成立于2020年,拥有4名员工。该公司位于马里兰州盖瑟斯堡317号埃灵顿大道323号,邮编:20878。

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