Tevogen.AI Builds Alpha Version of PredicTcell™ Model with Microsoft and Databricks; Observes Drastic Time Reduction in Target Analysis Translating to Potential Savings of Billions in Drug Development Costs
Tevogen.AI (NASDAQ:TVGNW), in collaboration with Microsoft and Databricks, has successfully developed the alpha version of its PredicTcell™ model, a groundbreaking AI platform for drug discovery. The model leverages machine learning and transformer architectures trained on terabyte-scale datasets of genetic and proteomic elements.
The platform has demonstrated significant efficiency improvements, reducing protein sequence analysis and peptide identification time from months to hours. Initially focused on virology datasets, Tevogen.AI plans to expand PredicTcell's application to oncology, potentially accelerating cancer immunotherapy development.
The company believes early adopters of AI-driven drug discovery could generate billions in revenue, while the technology could lead to substantial cost savings across the healthcare system by streamlining early-stage drug discovery and reducing wet lab dependency.
Tevogen.AI (NASDAQ:TVGNW), in collaborazione con Microsoft e Databricks, ha sviluppato con successo la versione alfa del suo modello PredicTcell™, una piattaforma AI innovativa per la scoperta di farmaci. Il modello utilizza tecniche di machine learning e architetture transformer addestrate su dataset di dimensioni terabyte contenenti elementi genetici e proteomici.
La piattaforma ha mostrato miglioramenti significativi in termini di efficienza, riducendo il tempo di analisi delle sequenze proteiche e l’identificazione dei peptidi da mesi a poche ore. Inizialmente focalizzata su dataset di virologia, Tevogen.AI prevede di estendere l’applicazione di PredicTcell all’oncologia, accelerando potenzialmente lo sviluppo di immunoterapie contro il cancro.
L’azienda ritiene che i primi utilizzatori della scoperta di farmaci basata su AI potrebbero generare miliardi di ricavi, mentre la tecnologia potrebbe portare a notevoli risparmi nei costi dell’intero sistema sanitario, semplificando la fase iniziale della scoperta di farmaci e riducendo la dipendenza dai laboratori sperimentali.
Tevogen.AI (NASDAQ:TVGNW), en colaboración con Microsoft y Databricks, ha desarrollado con éxito la versión alfa de su modelo PredicTcell™, una plataforma de IA revolucionaria para el descubrimiento de fármacos. El modelo aprovecha el aprendizaje automático y arquitecturas transformer entrenadas con conjuntos de datos de escala terabyte que contienen elementos genéticos y proteómicos.
La plataforma ha demostrado mejoras significativas en eficiencia, reduciendo el tiempo de análisis de secuencias proteicas e identificación de péptidos de meses a horas. Inicialmente centrada en conjuntos de datos de virología, Tevogen.AI planea ampliar la aplicación de PredicTcell a la oncología, acelerando potencialmente el desarrollo de inmunoterapias contra el cáncer.
La compañía cree que los primeros usuarios de la exploración de fármacos impulsada por IA podrían generar miles de millones en ingresos, mientras que la tecnología podría llevar a ahorros sustanciales en costos en todo el sistema de salud al optimizar la fase inicial del descubrimiento de fármacos y reducir la dependencia de laboratorios experimentales.
Tevogen.AI (NASDAQ:TVGNW)는 Microsoft 및 Databricks와 협력하여 혁신적인 신약 개발 AI 플랫폼인 PredicTcell™ 모델의 알파 버전을 성공적으로 개발했습니다. 이 모델은 유전자 및 단백질 요소의 테라바이트 규모 데이터셋을 기반으로 한 머신러닝과 트랜스포머 아키텍처를 활용합니다.
이 플랫폼은 단백질 서열 분석 및 펩타이드 식별 시간을 수개월에서 수시간으로 단축하는 등 효율성을 크게 향상시켰습니다. 초기에는 바이러스학 데이터셋에 집중했으며, Tevogen.AI는 PredicTcell의 적용 범위를 종양학으로 확장하여 암 면역치료제 개발을 가속화할 계획입니다.
회사는 AI 기반 신약 개발을 조기에 도입하는 기업들이 수십억 달러의 수익을 창출할 수 있을 것으로 보고 있으며, 이 기술이 신약 개발 초기 단계를 간소화하고 실험실 의존도를 줄여 의료 시스템 전반에 걸쳐 상당한 비용 절감 효과를 가져올 것으로 기대합니다.
Tevogen.AI (NASDAQ:TVGNW), en collaboration avec Microsoft et Databricks, a développé avec succès la version alpha de son modèle PredicTcell™, une plateforme d’IA révolutionnaire pour la découverte de médicaments. Ce modèle utilise l’apprentissage automatique et des architectures transformer entraînées sur des jeux de données génétiques et protéomiques de l’ordre du téraoctet.
La plateforme a démontré des améliorations significatives en termes d’efficacité, réduisant le temps d’analyse des séquences protéiques et d’identification des peptides de plusieurs mois à quelques heures. Initialement axée sur des jeux de données en virologie, Tevogen.AI prévoit d’étendre l’utilisation de PredicTcell à l’oncologie, accélérant potentiellement le développement d’immunothérapies contre le cancer.
L’entreprise estime que les premiers utilisateurs de la découverte de médicaments assistée par IA pourraient générer des milliards de revenus, tandis que cette technologie pourrait engendrer des économies substantielles dans le système de santé en rationalisant la découverte précoce de médicaments et en réduisant la dépendance aux laboratoires expérimentaux.
Tevogen.AI (NASDAQ:TVGNW) hat in Zusammenarbeit mit Microsoft und Databricks erfolgreich die Alpha-Version seines Modells PredicTcell™ entwickelt, einer bahnbrechenden KI-Plattform für die Wirkstoffentdeckung. Das Modell nutzt maschinelles Lernen und Transformer-Architekturen, die mit Terabyte-großen Datensätzen genetischer und proteomischer Elemente trainiert wurden.
Die Plattform hat erhebliche Effizienzsteigerungen gezeigt, indem sie die Analyse von Proteinsequenzen und die Peptididentifikation von Monaten auf Stunden verkürzte. Zunächst auf Virologie-Datensätze fokussiert, plant Tevogen.AI, die Anwendung von PredicTcell auf die Onkologie auszuweiten und so die Entwicklung von Krebsimmuntherapien zu beschleunigen.
Das Unternehmen ist der Ansicht, dass frühe Anwender der KI-gestützten Wirkstoffentdeckung Milliardenumsätze erzielen könnten, während die Technologie durch die Straffung der frühen Wirkstoffentdeckung und die Verringerung der Abhängigkeit von Nasslabors erhebliche Kosteneinsparungen im Gesundheitssystem ermöglichen könnte.
- Dramatic reduction in analysis time from months to hours for protein sequences and peptide identification
- Platform processes nearly a billion genetic and proteomic elements using terabyte-scale dataset
- Potential to generate billions in cost savings through streamlined drug discovery process
- Planned expansion into oncology applications could accelerate cancer immunotherapy development
- Strategic partnerships with major tech companies Microsoft and Databricks
- Platform is still in alpha version, indicating early development stage
- Requires significant capital investment for continued development and commercialization
- Faces potential regulatory challenges and uncertainties in commercial development
- Limited operating history increases execution risk
Insights
Tevogen's AI platform shows promising time/cost efficiencies in drug target discovery, representing a potential competitive advantage in pharmaceutical R&D.
Tevogen.AI has reached a significant milestone with its PredicTcell™ platform, built in collaboration with technology giants Microsoft and Databricks. The alpha version employs advanced machine learning and transformer architectures trained on terabyte-scale datasets with nearly a billion genetic and proteomic elements.
The platform's true value proposition lies in its dramatic acceleration of target analysis workflows. What previously took months in traditional pharmaceutical R&D can now be completed in hours through parallel processing and distributed computing. This represents a potential paradigm shift in early-stage drug discovery economics.
The architecture appears strategically designed to reduce dependency on expensive and time-consuming wet lab testing. By generating computational insights first, the company can prioritize only the most promising targets for physical validation, creating a force-multiplier effect for research budgets.
Currently focused on virology, Tevogen is expanding PredicTcell's application to oncology, indicating a platform approach rather than a single-disease solution. This suggests the technology has adaptability across multiple therapeutic areas, substantially increasing its long-term value potential.
While the announcement highlights an alpha version (suggesting early-stage development), the collaboration with established cloud computing leaders Microsoft and Databricks provides technical credibility. The mention of a complementary AdapTcell™ model for clinical trial optimization signals a broader AI ecosystem strategy that could address multiple pharmaceutical development bottlenecks.
The cost-efficiency gains in target discovery, if validated, could significantly impact Tevogen's competitive positioning in an increasingly AI-focused pharmaceutical landscape, where reducing time-to-market and development costs represent crucial advantages.
- Tevogen.AI’s model drastically reduces target analysis and has the potential to generate billions in cost savings across the healthcare system by streamlining early-stage drug discovery, reducing wet lab dependency, and accelerating timelines.
- Beyond cost savings, Tevogen.AI leadership believes AI-driven drug discovery has the potential to generate billions in top line revenues for companies who are early adopters.
- With virology datasets curated and alpha model created, Tevogen.AI will now apply PredicTcell to include oncology, expanding its scope and potentially accelerating cancer immunotherapy development.
WARREN, N.J., July 14, 2025 (GLOBE NEWSWIRE) -- Tevogen (“Tevogen Bio Holdings Inc.” or “Company”) (Nasdaq: TVGN) today announced that its artificial intelligence initiative, Tevogen.AI, in collaboration with Microsoft (Nasdaq: MSFT) and Databricks, has successfully built the alpha version of its foundational PredicTcell™ model.
PredicTcell is powered by a robust and scalable data engineering pipeline. Utilizing machine learning and transformer architectures trained on a terabyte-scale dataset encompassing nearly a billion genetic and proteomic elements, the model enhances target discovery. Through parallel processing and distributed computing, Tevogen.AI dramatically reduces protein sequence analysis and peptide identification from months to hours.
“This achievement underscores Tevogen.AI’s commitment to revolutionizing therapeutic development through AI-driven innovation,” said Mittul Mehta, Chief Information Officer and Head of Tevogen.AI. “By significantly accelerating identification of immunologically active targets, PredicTcell enables a more efficient transition into clinical research, ultimately benefiting patients. We look forward to enhancing our datasets to include the spectrum of virology, oncology and neurology to further enhance the PredicTcell platform.”
“Through the development and utilization of the PredicTcell platform we have uncovered new insights and are able to quickly analyze significantly larger datasets, potentially resulting in better accuracy and reduced time for wet lab testing,” said Dr. Neal Flomenberg, Chief Research and Scientific Officer.
Additional developments from Tevogen.AI’s platforms, including advancements in clinical trial optimization and patient market analysis through its complementary AdapTcell™ model, are planned to be announced in subsequent communications.
Forward Looking Statements
This press release contains certain forward-looking statements, including without limitation statements relating to: Tevogen’s plans for its research and manufacturing capabilities; expectations regarding future growth; expectations regarding the healthcare and biopharmaceutical industries; and Tevogen’s development of, the potential benefits of, and patient access to its product candidates for the treatment of infectious diseases and cancer. Forward-looking statements can sometimes be identified by words such as “may,” “could,” “would,” “expect,” “anticipate,” “possible,” “potential,” “goal,” “opportunity,” “project,” “believe,” “future,” and similar words and expressions or their opposites. These statements are based on management’s expectations, assumptions, estimates, projections and beliefs as of the date of this press release and are subject to a number of factors that involve known and unknown risks, delays, uncertainties and other factors not under the company’s control that may cause actual results, performance or achievements of the company to be materially different from the results, performance or other expectations expressed or implied by these forward-looking statements.
Factors that could cause actual results, performance, or achievements to differ from those expressed or implied by forward-looking statements include, but are not limited to: that Tevogen will need to raise additional capital to execute its business plan, which may not be available on acceptable terms or at all; changes in the markets in which Tevogen competes, including with respect to its competitive landscape, technology evolution, or regulatory changes; changes in domestic and global general economic conditions; the risk that Tevogen may not be able to execute its growth strategies or may experience difficulties in managing its growth and expanding operations; the risk that Tevogen may not be able to develop and maintain effective internal controls; the failure to achieve Tevogen’s commercialization and development plans and identify and realize additional opportunities, which may be affected by, among other things, competition, the ability of Tevogen to grow and manage growth economically and hire and retain key employees; the risk that Tevogen may fail to keep pace with rapid technological developments to provide new and innovative products and services or make substantial investments in unsuccessful new products and services; risks related to the ability to develop, license or acquire new therapeutics; the risk of regulatory lawsuits or proceedings relating to Tevogen’s business; uncertainties inherent in the execution, cost, and completion of preclinical studies and clinical trials; risks related to regulatory review, approval and commercial development; risks associated with intellectual property protection; Tevogen’s limited operating history; and those factors discussed or incorporated by reference in Tevogen’s Annual Report on Form 10-K.
You should not place undue reliance on forward-looking statements, which speak only as of the date they are made. Tevogen undertakes no obligation to update any forward-looking statements, except as required by applicable law.
Contacts
Tevogen Bio Communications
T: 1 877 TEVOGEN, Ext 701
Communications@Tevogen.com
FAQ
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