Verseon Corp.

Peer-reviewed paper describes novel ensembling technique that offers 150% improvement over current state of the art FREMONT, Calif., Oct. 9, 2024 /PRNewswire/ -- Verseon International Corporation is pleased to announce the peer-reviewed results for its fundamental innovation in combining machine-learning models. Combining models is known as "ensembling" in the AI field. Verseon has incorporated its novel class-weighted ensemble system (CWES) into its patent-pending VersAI™ technology. In the face of complex datasets, relying on a single AI model poses significant disadvantages that greatly diminish AI's predictive capabilities. Over the past 30 years, ensemble learning has emerged as a principal method to deal with datasets that are imbalanced, that contain a large number of noisy, scattered data points, or whose number of features is large relative to the number of observations within the data. However, combining individual models into a single coherent overarching paradigm still remains a significant challenge in the AI field. Among the most prevalent approaches for ensembling are simple voting, weighted-majority voting (WMVE), and the current state of the art known as class-specific soft voting (CSSV). In the paper Ensemble Learning with Highly Variable Class-Based Performance, Verseon's team describes their newly developed technique and contrasts it with simple voting, WMVE, and CSSV. Unlike other approaches, Verseon's CWES decides how to combine a set of models based on the predictions of each model. CWES excels at analyzing complex data because it dynamically assigns importance to the predictions of each model based on analysis of its performance on similar training data. Benchmarking reported in the paper shows that CWES outperforms other ensembling techniques on a wide range of datasets. "Given its significant performance improvement over the current state of the art, our approach marks a significant step forward in AI ensembling," said Verseon's Head of Machine Learning Ed Ratner. CWES has broad applicability to the vast majority of real-world scenarios, for which datasets are typically complex, sparse, or both. Because CWES makes such datasets more tractable, it greatly expands the utility of AI modeling in a broad range of scenarios. The field of small-molecule drug discovery is one such scenario. CEO Adityo Prakash said, "Our new ensembling approach demonstrates Verseon's dedication to making groundbreaking science and technology advancements whenever currently available tools prove inadequate for the tasks at hand. This commitment leads us to innovations that have widespread application in multiple fields. And it's how we continue to find promising drug candidates for the breakthrough treatments of tomorrow." About Verseon Verseon International Corporation () is a clinical-stage, technology-driven pharmaceutical company transforming the delay, prevention, and treatment of disease. Using its Deep Quantum Modeling + AI platform, Verseon is rolling out a steady stream of life-changing medicines. Each of the company's drug programs features multiple novel candidates with unique therapeutic properties. None of these candidates can be found by other current methods. Verseon's fast-growing pipeline addresses major human diseases in the areas of cardiometabolic disorders and cancers. The company's supporters and advisors include multiple Nobel laureates, former heads of R&D of major pharmaceutical companies, and various key opinion leaders in medicine. Media Contact Walter Jones [email protected] (510) 225-9000 SOURCE Verseon International Corporation WANT YOUR COMPANY'S NEWS FEATURED ON PRNEWSWIRE.COM? 440k+ Newsrooms & Influencers 9k+ Digital Media Outlets 270k+ Journalists Opted In GET STARTED

Innovative technologies to improve drug-candidate selection and clinical-trial design. SAN JOSE, Calif., July 30, 2024 /PRNewswire/ -- Verseon and NeuTherX are pleased to announce their partnership to accelerate drug development through the use of NeuTherX's scalable organoid development technology. Verseon continues to streamline every aspect of creating medicines that will improve how the world treats all major human diseases. Characterizing the behavior of drugs in humans has historically required extensive clinical trials, which entail considerable time and cost. Any lab-derived early insight into how drugs will behave in human patients can make drug trials vastly more efficient. To that end, Verseon has been developing organoid-based technology to characterize drugs. Organoids are artificially grown masses of cells that resemble the function of specific human organs. Testing drugs on organoids can deliver valuable information on the potential effects of drugs in real patients before any human is dosed. While Verseon continues to internally develop its groundbreaking suite of technologies for drug development, it also seeks out partners whose innovations complement its own. NeuTherX has created methods to rapidly cultivate organoids at scale. While the company has so far focused on psychiatric drug development, their expertise lends itself to the development of a wide range of organoids for other indications. Through this new partnership, NeuTherX's technology will augment Verseon's drug development process. "Verseon's platform continues to produce a steady stream of novel drug candidates," said Verseon's CEO Adityo Prakash. "We always strive to better characterize their potential behavior in human patients and in the process inform patient selection and improve overall trial design. NeuTherX's technology will be an excellent addition to our platform." NeuTherX's CEO Richard Hoopes added, "We envision organoid-based testing as the future of drug candidate selection. We are excited to partner with Verseon as they apply our technology across the whole spectrum of human diseases." ABOUT VERSEON Verseon International Corporation () is transforming the delay, prevention, and treatment of disease. Using its Deep Quantum Modeling + AI platform, Verseon is rolling out a steady stream of life-changing medicines. Each of the company's drug programs features multiple novel candidates with unique therapeutic properties. None of these candidates can be found by other current methods. Verseon's fast-growing pipeline addresses major human diseases in the areas of cardiometabolic disorders and cancers. The company's supporters and advisors include multiple Nobel laureates, former heads of R&D of major pharmaceutical companies, and various key opinion leaders in medicine. ABOUT NEUTHERX NeuTherX () is pioneering personalized treatments for neuropsychiatric disorders using patient-derived brain organoids. Our innovative platform models each patient's brain biology from somatic cells to develop precise therapies. By integrating vast data from deep biological analyses and advanced AI, we extract unique disease signatures for targeted drug selection and accelerated drug discovery. This approach aims to overcome the high failure rates of current treatments, providing effective, personalized solutions and enhancing clinical trials to deliver innovative therapies swiftly. Media Contact Walter Jones [email protected] (510) 225-9000 SOURCE Verseon International Corporation & NeuTherX

投资者每年向AI药物发现投入数十亿美元。最近的一项研究发现，在临床试验中有70多种通过AI研发出来的小分子、抗体和疫苗。但是目前还有一个亟待解决的问题就是知识产权，这一问题除不仅涉及药物，还涉及药物发现的方法。总部位于纽约市和香港的Insilico Medicine总裁Alex Aliper说。“每家公司都会对分子结构申请专利，至于算法就相对灵活了。”AI加速药物研发AI可以加速药物发现的多个阶段。例如，制药初创公司Verseon使用AI优化分子，并了解药物与基因在衰老和退化中的相互作用，机器学习部主管Ed Ratner说。Enveda Biosciences的首席执行官和创始人Viswa Colluru表示，他们应用几种类型的AI算法来预测来自质谱和高通量筛选数据的未知化合物的结构和功能。一些研究人员使用AI从零开始设计蛋白质。AI大大加快了药物发现的速度，并减小了成本。Insilico的Aliper表示，一般来说发现一种临床前候选药物可能需要数年时间和数千万美元，但他的公司在使用AI之后，仅用了18个月就提出了他们的候选药物ISM001-055，仅用了260万美元。鉴于它们的潜在优势，专利至关重要。Aliper表示，Insilico已经为大约50种AI方法申请了专利，除了他们对化合物的专利之外。根据剑桥大学生物医学创新、人工智能和法律研究主任Mateo Aboy的初步工作，截至目前，大学、大型制药公司、大型科技公司和初创企业等已经申请了约3000项与药物开发和交付相关的AI方法的专利。算法申请专利利与弊但是，算法很难申请专利。尽管美国专利与商标局规定专利可以授予“任何新的有用过程”，但算法（一系列步骤）不一定被视为过程。相反，法院通常将其视为抽象的概念，美国专利与商标局不允许就“抽象概念、自然法则和自然现象（包括自然产物）”申请专利。为了有资格获得专利，专利申请应描述所声称的发明如何整体地集成算法以解决特定的实际应用，从而改善技术或技术领域。仅仅说“在计算机上应用它”是不够的。2014年的一项具有里程碑意义的最高法院案件涉及Alice Corp.和CLS Bank，Alice Corp.起诉CLS Bank侵犯了他们的专利。即使在2019年美国专利与商标局修订了其指南之后，专利资格问题仍然不明朗。因此，今年六月，两名美国参议员提出了2023年的专利资格恢复法案，尚未通过，该法案明确将数学公式和心理过程列为不合格。Fish & Richardson的技术专家和专利代理Matthew Chun在最近的一篇预印本中为申请人提供了建议。发明者应该讨论一下他们如何训练他们的机器学习模型，描述方法的哪一部分不能由人类大脑完成，将创新框定为技术改进，将其与特定的机器联系起来，并且要具体说明。即使发明者认为他们的AI系统可能可以申请专利，他们也需要决定是否值得申请专利。Compound风投公司的投资者Shelby Newsad说：“对于许多公司来说，算法没有必要去申请专利，因为每隔几周就会有一个新的最先进的算法发布。”AI可以通过挖掘数据集（包括专利、论文、基因组和临床试验）来帮助识别靶点；它可以筛选分子或从零开始设计新的分子。在与包括自动驾驶车辆在内的技术合作的Global IP Counselors的专利律师和合伙人Xie表示，在自动驾驶车辆案例中，AI存在于产品中，因此首先将其申请专利非常重要。公开部署可能会不利于后续的专利申请。她表示，诺华这样的公司更感兴趣的是申请AI产生的药物，而不是AI本身。Aliper还表示，相比于方法，为药物申请专利的指导方针更加清晰。在决定是否申请专利一个AI系统，将其公开或将其保密时，发明者必须权衡多个因素。首先是申请的时间和精力。Gregory Rabin，一位来自Young Basile的专利律师表示，申请人是否会获得专利取决于审查员以及专利局对专利法的不断解释，也不清楚它是否会在法庭上被认可。在一些国家，即使申请失败，申请也会被公布。在美国，您可以要求在专利授权之前不公布申请，但授权之后专利也会在几年内公布，否则，专利申请将在提交后18个月内公布。Verseon的Ratner表示，当您申请专利一种AI系统时，您必须担心可发现性和可执行性。如果其他公司暗中采用了您专利的AI系统用于内部使用，很难发现侵权行为。由于您只能对一个想法的实现进行专利申请，而不能对裸露的想法进行专利申请，其他人可能会找到另一种实现您专利中的想法的方法，您将无法对他们执行专利。Ratner表示，决定是否申请专利是“一个商业问题——成本效益权衡”。Insilico的Aliper表示：“专利和知识产权保护有一种双重作用。它既可以是盾牌，也可以是剑。”他们可能会申请一种AI方法的专利，并对其他公司进行专利执行（尽管他们尚未这样做），或者申请专利以防止其他人对其进行专利并对他们进行执行。专利赋予他们自由并消除了业务上的不确定性。专利不需要提及一个AI系统在药物发现方面的应用，才能在该领域执行。Chun指出美国专利法中保护潜在侵权者的一些要素。其中之一规定，如果专利所有者等待时间过长才提起诉讼，他们可以获得的侵权赔偿可能会受到限制。另一个规定，如果专利所有者故意给出不打算执行专利的假象，他们可能会放弃以后执行专利的权利。Ratner表示，Verseon通常会使用在制药行业常见的已发表算法，因为已发表的算法在一年后不会被专利申请。Chun表示，专利律师有时会进行“自由操作”分析，搜索可能妨碍业务的专利。“根据我的经验，这些自由操作分析通常是由投资者提出要求发起的，”他说，“或者至少是因为客户知道投资者可能会提出这样的要求。”Colluru表示，Enveda保留了他们使用的算法的审计记录，以免触犯软件许可证。如果他们不喜欢某个工具的许可证，他们可能会自己开发替代方案。Rabin指出，一些公司实际上会避免进行专利搜索，因为有意侵犯专利会使损害赔偿额增加三倍。Aliper对新进入药物发现领域的人可能很难避免他人专利方法表示同情。他说，发明实际有用的方法是困难的。“这不像ChatGPT，你可以立即评估响应的质量。如果一个模型给出了一个分子结构，你需要在实验室中合成它，你需要在实验室中测试它，这需要时间和资源。”总的来说，AI专利很少被用于诉讼。2019年，世界知识产权组织报告称，与AI相关的专利家族超过30万个，截至2017年，不到半个百分点的专利曾在诉讼案中被提及。参考资料：Hutson, M. AI for drug discovery is booming, but who owns the patents?. Nat Biotechnol 41, 1494–1496 (2023). https://doi.org/10.1038/s41587-023-02029-7版权信息本文系AIDD Pro接受的外部投稿，文中所述观点仅代表作者本人观点，不代表AIDD Pro平台，如您发现发布内容有任何版权侵扰或者其他信息错误解读，请及时联系AIDD Pro (请添加微信号sixiali_fox59)进行删改处理。本文为原创内容，未经授权禁止转载，授权后转载亦需注明出处。有问题可发邮件至sixiali@stonewise.cn关注我，更多资讯早知道↓↓↓