Hi guys! How are you all? Have you had a chance to register for DataFAIR Global on March 12? This virtual-only event is the digital precursor to our flagship DataFAIR Boston summit on March 19. We will discuss how to measure progress in AI for Drug Discovery: The Turing Test for Drug Discovery. Specifically, we will be asking our leaders to -
Score the Problems: Which end-to-end tasks are the most valuable to solve from a strategic and economic standpoint?
Define the "Pass" Criteria: What must AI achieve to complete these tasks successfully even with a human-in-the-loop to be considered transformative?
Event Details Date: March 12, 2026 (Virtual) Agenda & Registration: https://www.elucidata.io/datafair-2026-spring You can register here to join
File competitive RFPs and compress tech transfer timelines by modernizing your data infrastructure. We are hosting an interactive session to show exactly how agent-based AI workflows contribute directly to revenue goals. Current challenges in the CDMO industry rely on data accuracy. From modeling plant capacity to ensuring gene vector purity, success requires a shift from static reports to dynamic knowledge graphs. You will learn how to:
Accelerate Tech Transfer: Move molecules from the lab to production faster.
De-risk Operations: Validate data to stop manual entry errors and batch failures.
Ensure Compliance: Use GxP-compliant AI that documents every decision for auditors.
Hey folks, For most of history, menstrual blood has been treated as something to hide and never as data. With NextGen Jane’s tampon-based multi-omics approach, menstrual fluid is now becoming a valuable diagnostic resource. Stephen Gire, CSO at NGJ, said in an article. “With molecular tools, physicians could potentially diagnose and treat earlier, drug developers could find new targets, leading to more-refined therapeutics, and run more-efficient clinical trials.” Read more. We are excited to have him join our experts for our upcoming webinar, Building a Predictive Diagnostic Model from Menstrual Fluid Data. We will break down new insights and practical ways to work with a sample type that has hardly been studied before. Learn how to turn scattered clinical notes, symptom histories, and EMR data into a unified picture alongside menstrual‑omics and see a concrete framework for connecting longitudinal patient records to new datasets so your predictive models are reliable enough, straight from the experts who have already done it. Date - December 2nd, 2025 Time - 9 AM PT Register Now to Join Menstrual fluid is one of the richest and untapped biological samples in modern medicine. It carries genomic, transcriptomic, proteomic, and immune signatures that can reshape how we diagnose conditions earlier and more accurately. Your insights, skepticism, and questions can enrich a conversation that has been absent from mainstream biomedical research. If relevant, please circulate it within your network.
Hey folks! Developing new therapies for autoimmune diseases like RA and lupus is a significant challenge, despite a $160B+ global market need. Many translational teams struggle to build a robust biomarker strategy, leading to delays in IND filings and trial initiation. Our upcoming session will show how a distinctive combination of technology and expertise equips your team to:
Validate the most impactful biomarker endpoints for your specific drug candidate.
Integrate patient-reported outcomes (PROs) with biomarkers to lower trial cost and reduce patient burden.
Design an informed, de-risked Phase 1 trial that accelerates your path to approval.
To share how leading biopharma teams are building these data-driven strategies, we invite you to this session. Register Now to request your invite
Hey folks! Clinical and real-world datasets hold enormous potential for discovery and trials, but the data rarely aligns. Patient records remain fragmented, inconsistently defined, and siloed across labs, imaging, omics, and outcomes. In fact, most EHRs capture little more than demographics and diagnoses but leave high-resolution measurement and molecular context out of reach. What's the cost?
Weeks (sometimes months) lost pulling data just to define a cohort.
Gaps that skew models and introduce bias.
Slowdowns in translational pipelines, biomarker validation, and trial readiness.
What if you could bring patient and molecular data into one knowledge graph that’s ready to use? Join us for Incorporating “Patient Data” into Knowledge Graphs, a practical session on how to turn heterogeneous clinical data into a coherent, privacy-preserving, and update-ready knowledge graph that accelerate translational research. Date: September 18, 2025 Time: 10:30 AM PST / 1:30 PM EST Register Now to request your invite!
Hey folks! Clinical and real-world datasets hold enormous potential for discovery and trials, but the data rarely aligns. Patient records remain fragmented, inconsistently defined, and siloed across labs, imaging, omics, and outcomes. In fact, most EHRs capture little more than demographics and diagnoses but leave high-resolution measurement and molecular context out of reach. What's the cost?
Weeks (sometimes months) lost pulling data just to define a cohort.
Gaps that skew models and introduce bias.
Slowdowns in translational pipelines, biomarker validation, and trial readiness.
What if you could bring patient and molecular data into one knowledge graph that’s ready to use? Join us for Incorporating “Patient Data” into Knowledge Graphs, a practical session on how to turn heterogeneous clinical data into a coherent, privacy-preserving, and update-ready knowledge graph that accelerate translational research. Date: September 18, 2025 Time: 10:30 AM PST / 1:30 PM EST Register Now to request your invite!
Hey everyone! Generic LLMs (e.g., GPT-4) can parse biomedical documents, they still do not deliver the structured, trustworthy datasets discovery teams need. Polly Xtract, our newly built multi-agent AI, is purpose-built for biomedical complexity. It converts unstructured trial data into analysis-ready schemas you can query in minutes. It works across PDFs, images, free text, and messy tables while preserving full traceability. Teams see 4x higher throughput with complete evidence logs. Get early access to Polly Xtract and see it live at our upcoming webinar with Hatim Zariwala (Faculty, Boston University; former Head of CNS Research, Stealth BioTherapeutics) and Shubhra Agrawal (Senior Scientific Manager, Elucidata). This matters across discovery and becomes critical in CNS programs where drugs must cross the blood–brain barrier. Teams need fast, comparable PK/PD signals to track competitors and know how competing drugs are performing in the market. Can’t attend live? Register for the webinar and get early access of Polly Xtract to try it out on your own projects.
Hey everyone! Generic LLMs (e.g., GPT-4) can parse biomedical documents, they still do not deliver the structured, trustworthy datasets discovery teams need. Polly Xtract, our newly built multi-agent AI, is purpose-built for biomedical complexity. It converts unstructured trial data into analysis-ready schemas you can query in minutes. It works across PDFs, images, free text, and messy tables while preserving full traceability. Teams see 4x higher throughput with complete evidence logs. Get early access to Polly Xtract and see it live at our upcoming webinar with Hatim Zariwala (Faculty, Boston University; former Head of CNS Research, Stealth BioTherapeutics) and Shubhra Agrawal (Senior Scientific Manager, Elucidata). This matters across discovery and becomes critical in CNS programs where drugs must cross the blood–brain barrier. Teams need fast, comparable PK/PD signals to track competitors and know how competing drugs are performing in the market. Can’t attend live? Register for the webinar and get early access of Polly Xtract to try it out on your own projects.
PK/PD teams live in the details such as pulling dose/exposure tables, calculating Cmax and AUC, interpreting half-life, clearance, and safety profiles. When this data stays buried in clinical trial PDFs, it can:
Delay competitive assessments by 1 - 2 weeks
Push back dose and regimen decisions, which can delay trial starts by months
Give competitors first access to trial sites, patients cohorts, and regulatory momentum
In CNS or oncology, even a 2–3 day delay can cost millions or force late market entry. What if you could find that competitive PK/PD intel in minutes? Consider a new CNS drug: to gauge its prospects, you need to know how similar drugs crossing the blood–brain barrier are performing. That critical intel is out there in clinical trial reports and publications, but it’s buried in PDFs and unstructured tables. To learn how AI-powered PK/PD data extraction can get you a newer intel faster, we invite you to our upcoming webinar “AI-powered Insights from PK/PD Clinical Trial Data.” This live session will include a real-time demo of Polly Xtract and a discussion with experts on applying multi-agent AI in drug development. You will discover how teams are achieving 4× faster data curation and turning unstructured trial data into AI-powered insights. Register now to request your invite.
