{"id":925802,"date":"2026-01-09T08:25:14","date_gmt":"2026-01-09T13:25:14","guid":{"rendered":"https:\/\/www.marketnewsdesk.com\/index.php\/revvity-collaborates-with-lilly-to-expand-access-to-ai-drug-discovery-models\/"},"modified":"2026-01-09T08:25:14","modified_gmt":"2026-01-09T13:25:14","slug":"revvity-collaborates-with-lilly-to-expand-access-to-ai-drug-discovery-models","status":"publish","type":"post","link":"https:\/\/www.marketnewsdesk.com\/index.php\/revvity-collaborates-with-lilly-to-expand-access-to-ai-drug-discovery-models\/","title":{"rendered":"Revvity Collaborates with Lilly to Expand Access to AI Drug Discovery Models"},"content":{"rendered":"

<\/p>\n

Revvity Collaborates with Lilly to Expand Access to AI Drug Discovery Models<\/b><\/p>\n

Collaboration funds participating biotechs to access Lilly predictive models through the Revvity Signals Xynthetica offering<\/i><\/p>\n

WALTHAM, Mass.–(BUSINESS WIRE<\/a>)–Revvity, Inc.<\/a> (NYSE: RVTY) today announced a collaboration with Eli Lilly and Company<\/a> (Lilly) that expands access to Lilly\u2019s high-quality predictive models by making Lilly TuneLab available through the Revvity Signals platform<\/a>. The collaboration builds on Revvity\u2019s recently introduced Signals XyntheticaTM<\/sup> offering<\/a>, creating a scalable, federated framework designed to accelerate AI-enabled drug discovery.<\/p>\n

Lilly TuneLab<\/a> was established to make advanced AI\/ML models, trained on decades of Lilly research data, available to the broader biotech community in exchange for data contributions that improve model performance through federated learning. The Signals Xynthetica platform operationalizes the delivery of the Lilly TuneLab models directly within the Signals platform, where experimental data is already captured, curated, and analyzed by thousands of discovery teams worldwide.<\/p>\n

\n\u201cFederated learning represents one of the most powerful paths forward for AI in drug discovery, but it requires the right platform to succeed,\u201d saidKevin Willoe, president of Revvity Signals Software. \u201cBy providing access to Lilly\u2019s worldclass predictive models through the Signals Xynthetica platform, we are creating a practical, secure way for organizations of all sizes to contribute to, and benefit from, collective intelligence.\u201d<\/p>\n

\nThrough Revvity\u2019s Models-as\u2011a\u2011Service framework and its Signals Xynthetica offering, participating organizations will be able to apply Lilly\u2019s predictive models to their own discovery programs while keeping proprietary data private and secure. This approach amplifies the value of contributions from small and midsized\u2011biotechs, whose diverse experimental data can significantly enhance collective predictive performance.<\/p>\n

\nThe Signals platform provides critical infrastructure to enable organizations to access the Lilly TuneLab models, including scientific data management, analytics, and collaboration across internal and external partners. Revvity\u2019s Signals OneTM<\/sup><\/a> offering already supports wet-lab data capture and orchestration across experiments, and its Signals SynergyTM<\/sup><\/a> module enables secure data exchange with CROs, academic collaborators, and partners, capabilities that are essential for scalable federated learning networks.<\/p>\n

\nAs part of the collaboration, Lilly and Revvity will jointly fund access for selected participants, including access to Signals One and Signals Xynthetica software offerings and modeling credits. This co-funded model underscores both companies\u2019 commitment to lowering barriers to AI adoption and accelerating innovation across the biotech ecosystem.<\/p>\n

About Revvity
\n
<\/b>At Revvity, \u201cimpossible\u201d is inspiration, and \u201ccan\u2019t be done\u201d is a call to action. Revvity provides health science solutions, technologies, expertise and services that deliver complete workflows from discovery to development, and diagnosis to cure. Revvity is revolutionizing what\u2019s possible in healthcare, with specialized focus areas in translational multi-omics technologies, biomarker identification, imaging, prediction, screening, detection and diagnosis, informatics and more.<\/p>\n

\nWith 2024 revenue of more than $2.7 billion and approximately 11,000 employees, Revvity serves customers across pharmaceutical and biotech, diagnostic labs, academia and governments. It is part of the S&P 500 index and has customers in more than 160 countries.<\/p>\n

\nStay updated by following our Newsroom<\/a>, LinkedIn<\/a>, X<\/a>, YouTube<\/a>, Facebook<\/a> and Instagram<\/a>.<\/p>\n

\"\"<\/p>\n

View source version on businesswire.com: <\/span>https:\/\/www.businesswire.com\/news\/home\/20260109127353\/en\/<\/a><\/span><\/p>\n

Investor Relations:
\n<\/b>
Steve Willoughby
\n
steve.willoughby@revvity.com<\/a><\/p>\n

Media Relations:
\n<\/b>
Chet Murray
\n
(781) 462-5126
\n
chet.murray@revvity.com<\/a><\/p>\n

KEYWORDS:<\/b> United States North America Massachusetts<\/p>\n

INDUSTRY KEYWORDS:<\/b> Hardware Health Technology Data Management Consumer Electronics Technology Pharmaceutical Professional Services General Health Medical Devices Artificial Intelligence Science Biotechnology Data Analytics Health Research<\/p>\n

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Revvity Collaborates with Lilly to Expand Access to AI Drug Discovery Models Collaboration funds participating biotechs to access Lilly predictive models through the Revvity Signals Xynthetica offering WALTHAM, Mass.–(BUSINESS WIRE)–Revvity, Inc. (NYSE: RVTY) today announced a collaboration with Eli Lilly and Company (Lilly) that expands access to Lilly\u2019s high-quality predictive models by making Lilly TuneLab available through the Revvity Signals platform. The collaboration builds on Revvity\u2019s recently introduced Signals XyntheticaTM offering, creating a scalable, federated framework designed to accelerate AI-enabled drug discovery. Lilly TuneLab was established to make advanced AI\/ML models, trained on decades of Lilly research data, available to the broader biotech community in exchange for data contributions that improve model performance through federated learning. The Signals Xynthetica platform … <\/p>\n

Continue reading “Revvity Collaborates with Lilly to Expand Access to AI Drug Discovery Models”<\/span><\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-925802","post","type-post","status-publish","format-standard","hentry"],"yoast_head":"\nRevvity Collaborates with Lilly to Expand Access to AI Drug Discovery Models - Market Newsdesk<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.marketnewsdesk.com\/index.php\/revvity-collaborates-with-lilly-to-expand-access-to-ai-drug-discovery-models\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Revvity Collaborates with Lilly to Expand Access to AI Drug Discovery Models - Market Newsdesk\" \/>\n<meta property=\"og:description\" content=\"Revvity Collaborates with Lilly to Expand Access to AI Drug Discovery Models Collaboration funds participating biotechs to access Lilly predictive models through the Revvity Signals Xynthetica offering WALTHAM, Mass.–(BUSINESS WIRE)–Revvity, Inc. (NYSE: RVTY) today announced a collaboration with Eli Lilly and Company (Lilly) that expands access to Lilly\u2019s high-quality predictive models by making Lilly TuneLab available through the Revvity Signals platform. The collaboration builds on Revvity\u2019s recently introduced Signals XyntheticaTM offering, creating a scalable, federated framework designed to accelerate AI-enabled drug discovery. Lilly TuneLab was established to make advanced AI\/ML models, trained on decades of Lilly research data, available to the broader biotech community in exchange for data contributions that improve model performance through federated learning. 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