The initial days following an advanced cancer diagnosis are critical for strategic treatment planning, yet clinicians have traditionally been forced to operate in an information vacuum while awaiting comprehensive genomic profiling results. Tempus Preview fundamentally redefines this diagnostic timeline. By combining Tempus’ multimodal data and advanced AI capabilities applied directly to the earliest touchpoints of the laboratory workflow, Tempus equips care teams to access early, clinically significant, information that can help inform complex decisions for patients and shorten the time between receipt of final molecular results and implementation of a personalized treatment plan.

At launch, Tempus Preview will focus exclusively on high-impact biomarkers where early insights can be critical, including:

• Surfacing patients more likely to harbor microsatellite instability (MSI-H), a biomarker linked to improved response to immune checkpoint inhibitors and potential hereditary risk factors, in colorectal, endometrial, prostate, and esophagogastric cancers.
• Predicting EGFR mutations in non-small cell lung cancer (NSCLC), a biomarker that infers response to targeted therapy, but often lacks response to frontline immunotherapy.
• Highlighting increased probability of potential rare, yet clinically significant FGFR fusions in hepatobiliary and bladder cancers, that may indicate potential response to targeted therapy and improved patient prognosis if gene fusions are present.

Also Read: Benchling and Baseten Partner to Bring AI Inference to Biotech R&D

Shortly thereafter, Tempus Preview will expand to other critical biomarkers.

Tempus Preview’s biomarker predictions are powered by Paige Predict, an advanced AI model that analyzes standard H&E images to provide genomic insights. Paige Predict, trained on millions of slides, has been validated for clinical use as part of Tempus’ laboratory-developed test.

“At Tempus, our unique combination of a diagnostic lab and an advanced data platform enables us to build AI models powered by our unparalleled depth of real-world data,” said Eric Lefkofsky, Founder and CEO of Tempus. “That foundation creates a powerful flywheel: every insight strengthens our models, and every model helps generate more clinically meaningful insights for providers and patients. Tempus Preview brings this intelligence directly into the clinical workflow, delivering early, clinically relevant information within one day of sample receipt, which for many patients can mean the difference in how they are treated. This is our AI flywheel in action: transforming complex information into timely insights, delivered to physicians when they need them most.”

Source: BusinessWire

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Built on Biocytogen’s proprietary RenMice^® platforms, RenSuper establishes a closed-loop antibody discovery engine combining in vivo immune repertoires, AI-driven candidate selection, automated experimental validation, and scalable manufacturing infrastructure. The platform supports the discovery and development of monoclonal antibodies, bispecifics, multispecifics, antibody-drug conjugates (ADCs), VHHs, and other advanced therapeutic modalities, significantly reducing the time and risk associated with antibody discovery.

RenSuper Workstation: AI-Powered One-Click Target-to-Lead Therapeutic Antibody Discovery

RenSuper Workstation introduces a new paradigm for one-click therapeutic antibody discovery, transforming antibody discovery from a project-based workflow into a searchable and programmable system. The platform enables rapid identification of high-quality therapeutic candidates with strong developability, functional relevance, and translational potential.

Proprietary Fully Human Antibody Library

The platform provides off-the-shelf access to fully human antibody sequences against more than 1,000 validated targets, complete with data packages to support more confident decision-making.

AI-Powered Candidate Selection

RenSuper’s AI models are trained on more than 100 million biologically validated antibody sequences, enabling efficient candidate identification against structurally and biologically challenging targets.

Using Biocytogen’s RenNano^® fully human HCAb platform as an example, the AI-powered screening workflow achieved an average positive hit rate of 46%, with peak hit rates reaching 98%. The workflow also enriched candidates with superior developability profiles and delivered an 82% success rate for high-purity (≥90%) Fc-format antibody expression.

Also Read: QIAGEN Launches QIA Agent to Bring AI-Driven Scientific Guidance into Research Workflows 

Experimentally Validated Antibody Sequences

All AI-selected antibody sequences undergo experimental validation, including binding, affinity, and specificity characterization, ensuring reproducibility, reliability, and downstream development readiness.

One-Click Search & Design

The platform enables one-click filtering and comparison of antibody candidates by target, epitope, and physicochemical properties. An integrated antibody design platform further supports rapid sequence assembly and flexible format design across multiple therapeutic modalities.

RenSuper High-Throughput Automation Center

Beyond intelligent discovery, RenSuper integrates industrial-scale automation to establish a scalable, closed-loop antibody development engine.

The newly launched automation center enables high-throughput experimental validation of AI-selected antibody candidates with minimal manual intervention, significantly accelerating the transition from molecular design to functional evaluation.

The fully automated platform spans the entire protein production workflow, including bacterial inoculation, plasmid extraction, transfection, fed-batch cultivation, and purification.

The system consistently delivers antibody yields averaging 50-100 mg, with throughput of up to 800 samples per day and over 5,000 samples per week, transforming antibody validation from a labor-intensive process into a scalable industrial workflow.

“We believe the future of antibody discovery will be driven by speed, scale, and data-driven intelligence,” said Dr. Yuelei Shen, Founder and CEO of Biocytogen. “With RenSuper, we are building an intelligence-native system that makes antibody discovery more intelligent, scalable, and efficient. Our goal is to reduce the time and complexity of early-stage discovery, accelerate the development of innovative therapeutics, and bring new medicines to patients faster.”

Together, RenSuper Workstation and the RenSuper High-Throughput Automation Center establish a next-generation target-to-lead ecosystem powered by biologically validated immune repertoires, artificial intelligence, and industrial-scale automation.

This milestone further reinforces Biocytogen’s vision of becoming the “Global Headstream of New Drugs” by transforming therapeutic antibody discovery into a more intelligent, scalable, and programmable process for biologics innovation worldwide.

Source: BusinessWire

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The QIAGEN team believes that QIA Agent allows for getting information on science, receiving product recommendations, accessing relevant data on workflow planning, accessing technical documentation, and obtaining other assistance in managing experiments using one conversational interface. With QIA Agent’s help, scientists will be able to plan their experimentations, get recommendations on choosing products for it, compare solutions available, obtain access to protocols, and get more information on any technical issues.

It should be noted that QIA Agent operates as part of the whole QIAGEN digital ecosystem and works in connection with such applications as Experiment Configurator, Product Availability Checker, and Order Status Checker. As QIAGEN’s team claims, the main purpose of this application is to make the way from the beginning to completion of an experiment easier for a researcher.

Nitin Sood, Senior Vice President and Head of Product Portfolio & Innovation at QIAGEN, noted that researchers are facing increasing scientific complexity, growing volumes of data, and expanding workflow choices. The new platform is designed to address these challenges through a unified AI-powered experience that streamlines access to scientific and operational information.

Also Read: Virtualitics and OpenAI Join Forces to Develop Next-Generation AI Readiness Solutions

AI Expands Its Role in Scientific Research and Laboratory Technology

The release of QIA Agent marks an emerging trend within the life sciences and tech sectors. Typically, researchers would be required to engage with a variety of software environments, databases, technical documentation, and searching procedures to navigate their workflow. However, the emergence of generative artificial intelligence and agentic systems now allows organizations to build intelligent assistants that will not only be able to understand the context of their work but synthesize relevant data and assist users with completing complex scientific experiments.

For the IT sector, the emergence of such solutions signifies another step towards the growing convergence of AI technologies with scientific computing, cloud research infrastructure, and corporate knowledge management. Indeed, the emergence of specialized software with built-in AI assistants can be expected to lead to a growing need for software solutions based on AI infrastructure and data platforms, knowledge graphs, and domain-specific AI capabilities.

From a broader perspective, the emergence of AI assistants is part of an ongoing process whereby artificial intelligence technologies are being incorporated into scientific research. It is quite obvious that vendors in the health, biotech, and pharma industries will seek to enhance their capabilities by developing AI solutions to facilitate scientific research.

Business Impact and Industry Outlook

For companies working in life sciences, diagnostics, pharmaceuticals, and other research-oriented industries, an artificial intelligence-based scientific assistant would make a major contribution to the optimization of the workflow and the increase of productivity. Often, researchers dedicate a considerable amount of time to the search for protocols, technical descriptions, products, and the coordination of work processes. Using such an intelligent system would allow companies to cut down administrative costs and let researchers focus on research.

Additionally, with such an assistant, a company might achieve faster research times through easier and more effective access to scientific knowledge. With better information access and workflow organization, researchers will be able to conduct experiments faster and more efficiently.

However, the increased use of AI in scientific laboratories raises important issues related to the explanation of actions performed, especially since they can have both scientific and economic consequences. Therefore, it is essential that the assistance provided by AI be based on human input and expertise.

The Future of AI-Connected Scientific Workflows

QIAGEN’s QIA Agent launch highlights a broader transformation underway across the scientific technology landscape. As AI agents become increasingly capable of understanding scientific context and supporting complex workflows, research platforms are evolving from static software tools into intelligent assistants that actively guide users through discovery and operational processes.

For the IT industry, this development signals a future where AI-powered domain-specific assistants become standard components of enterprise software ecosystems. Organizations that successfully integrate these technologies may gain advantages in research productivity, operational efficiency, and innovation speed as AI continues to reshape the future of scientific computing and digital transformation.

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Between 2020 and 2025, the number of new scientific AI models released annually grew from 28 to more than 380. These models are now standard in R&D workflows, but the compute layer hasn’t kept up. Drug discovery is bursty by nature: teams wait on the physical lab, data comes in waves, then need to run 100,000 predictions in a few hours before going quiet for days. For most computational teams, that plays out as HPC queues with multi-week backlogs, GPU reservations sitting idle between data collection cycles, and predictions rationed during active campaigns.

Benchling Inference is built on the Baseten Inference Stack, a tightly integrated combination of a high-performance runtime (custom kernels, speculative decoding, KV cache optimizations) and inference-optimized infrastructure spanning 15+ cloud providers, with cold starts in 5–10 seconds. Benchling adds a biotech layer on top with pre-configured defaults for scientific models and deployment options for organizations with strict data residency requirements. By aggregating demand across the industry, Benchling also brings better economics to biotech startups.

Also Read: Owkin to Build AI Agents as Part of a Multi-Year K Pro License Agreement With AstraZeneca

With Benchling Inference, scientists can deploy third-party models or serve internal models built on their own experimental data from a unified compute environment. For teams with data sovereignty requirements, the Baseten Inference Stack runs identically in Baseten Cloud, inside a customer’s virtual private cloud (VPC), or a hybrid of both so predictions never have to leave their environment. Computational scientists working in Jupyter notebooks or via SDK can call inference directly through Benchling.

“Biotech has entered a new era where AI models trained on proprietary experimental data could unlock breakthroughs that weren’t possible before. The bottleneck has been infrastructure and biotech research labs should not have to become GPU experts to run frontier models on their data. By partnering with Benchling, we bring six years of inference expertise directly into the environments where the science happens” said Amir Highighat, CTO & Co-Founder of Baseten.

“Access to compute is becoming a strategic advantage. But we hear from computational scientists that getting inference to work in drug discovery is harder than it should be; workloads are bursty, the data is sensitive, compute costs are too high,” said Ashu Singhal, co-founder and President of Benchling. “We’ve been running Baseten internally for Benchling’s Model Hub and learned a lot about tailoring inference for drug discovery. Now we want customers to have the same access.”

Source: PRNewswire

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Also Read: IBM Marks a Decade of Quantum Computing on the Cloud, Driving Global Innovation

This model helps speed up catalyst discovery by quickly calculating adsorption energy. This is crucial for evaluating catalyst performance in sustainable fuels, hydrogen, and recycling plastics. SandboxAQ also revealed plans to bring additional pharmaceutical-focused models, including AQPotency and AQCell, to Claude in the near future. “Now, researchers can access frontier physics-based models directly inside the AI tools they already use, with no additional infrastructure, code or barriers,” said Jack D. Hidary, CEO of SandboxAQ. Industry experts noted that combining conversational AI with quantitative scientific models could dramatically shorten discovery timelines and improve accessibility for researchers across scientific disciplines.

Read More: SandboxAQ Integrates its Quantitative AI Models with Anthropic’s Claude via MCP

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Under the three-year licensing agreement, Owkin will lead the end-to-end development of AI agents to run on K Pro, integrated within AstraZeneca’s IT infrastructure and decision workflows. The new agents’ functionality is intended to help AstraZeneca’s decision-making teams access timely, data-rich insights for complex competitive intelligence questions, reducing reliance on manual analysis within established governance, security, and enterprise standards.

Also Read: Sapio Sciences Brings Claude Cowork to the Lab

“At Owkin, we believe the future of the pharmaceutical industry is agentic,” said Thomas Clozel, CEO and co-founder of Owkin. “Our experience, multimodal data, and agentic infrastructure allows us to build various complex agents supporting our pharmaceutical partners, including competitive intelligence agents to support quick decisions by executives.”

This new agreement builds on Owkin’s previous work with AstraZeneca to develop an AI gBRCA pre-screen solution for breast cancer. Ongoing results from this project were published at ESMO where the BRCAura RUO solution was found to rule out approximately 40% of patients unlikely to carry gBRCA mutations with a high sensitivity of 93%¹. This work now continues at Waiv, the recent spin-out of Owkin’s diagnostic division, while Owkin continues to focus on its core aim – developing biological artificial superintelligence to understand complex biology.

Source: BusinessWire

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Also Read: Clinical AI Launches MAIA Prescreening on Google Cloud Marketplace to Accelerate Clinical Trials 

“We are pleased to see the FDA validate the advantages that have been clear to us from our first engagements with NanoMosaic,” said Gaurav Venkataraman, Trisk Bio CEO. “Trisk is oriented around helping our clients become successful by providing high-quality material and de-risking manufacturing from the start. Legacy AAV analytics can misread critical quality attributes by orders of magnitude. It’s critical that our clients’ programmes rest on technology yielding the most accurate possible measurements.”

Read More: Trisk Bio and NanoMosaic Announce Partnership for Advanced AAV Analytics

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Also Read: Roche Expands AI Capabilities with NVIDIA-Powered Factory to Accelerate Drug Discovery

“AI can design new protein molecules for biologic drugs: the problem is that reality doesn’t always reflect what the computer imagines,” said André Goerke, CEO of LenioBio. “Bringing together Twist’s highly automated manufacturing and characterization capabilities with LenioBio’s ALiCE® system for cell-free protein expression, can enable rapid data generation from real-world molecules.” The companies believe the collaboration will help accelerate AI-led antibody development and improve experimental accuracy in biologics research.

Read More: LenioBio and Twist Bioscience Enter into a Collaboration to Further Enable AI drug-discovery

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Working autonomously on behalf of the user, Claude Cowork searches across data sources, collates findings and returns verified, structured outputs, reports and dashboards. Connected to the Sapio Platform, Claude Cowork can also take actions on the user’s behalf to operate on ELN experiments or LIMS processes and data, and where it does, all actions are executed with full traceability with attribution to the requesting user.

Kevin Cramer, CEO and Founder, Sapio Sciences, commented, “Sapio Elain is the AI co-scientist inside the Sapio Platform, making every interaction smarter for the scientist at the bench. Claude acts as an extension of Elain’s capabilities, opening up new reporting and analytical possibilities and enabling action on data across the entire organization. Together, they give our customers AI that works at every level of the organization, all from a single prompt.”

Also Read: Drug Hunter Launches Molecule Search: A Unified Structure Search Experience Across Chemical and Biological Data 

Addressing the needs of scientists and project leaders

For scientists, this removes a significant bottleneck in the research process. Questions that span multiple processes or experiments have typically required multiple searches, manual data exports and time spent waiting for computational support for analysis. Claude Cowork answers those questions in a single prompt, retrieving, collating and analyzing across the full Sapio data environment.

For project leaders and team managers, it provides something equally valuable: visibility across an entire program without logging into the platform. From real-time project status to which experiments are complete or where the bottlenecks are, Claude Cowork assembles that picture on demand from a single question.

R&D intelligence, accessible from a single prompt

Connected to the Sapio Platform via Sapio Elain, Claude Cowork returns verified, structured outputs, including dashboards, HTML reports and research summaries. Example use cases include:

• Cross-experiment analysis: Find all experiments related to a specific molecule, compare synthesis conditions across runs, identify which approaches produced the best yields, and surface the optimal parameters for the next experiment.
• Project data analysis: Pull all activity data for a specific target or project, run SAR trend analysis across compound series, and return a structured summary of the most promising candidates to progress.
• Program and project tracking: Get a real-time view of where a program stands, which experiments are complete, which are stalled, and which team members have outstanding tasks, from a single prompt.
• Compliance and lab operations: Report on unsigned experiments past due, generate reagent inventory reports with reorder and expiry alerts, and produce KPI dashboards across active projects.
• Compound registration and platform actions: Register compounds directly into the Sapio Platform from an SDF file provided by a CRO in email, all with full traceability.

Rob Brown, VP and Head of the Scientific Office, Sapio Sciences, commented, “Scientists and project leaders spend too much time hunting for information that already exists across their organizations. Whether that data lives across experiments, across teams or buried in email, Claude Cowork gives them a single conversation to find it, analyze it and act on it. That is a meaningful shift in how R&D teams operate day to day.”

Customers can also connect Sapio Elain to any supported AI assistant, including Microsoft Copilot and ChatGPT, giving organizations the flexibility to work with their preferred tools.

Source: BusinessWire

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Structure-based research has long required scientists to navigate multiple tools and disconnected databases. This release consolidates those workflows into a single, more intuitive experience as the platform now pairs chemical matter with biological context, including indexed targets, mechanisms of action, 3D structure information, and therapeutic areas, without requiring users to leave the platform.

“Scientists increasingly need to explore chemical and biological data together,” said Dennis Hu, Ph.D., Founder and CEO of Drug Hunter. “This update brings discovery teams a ‘one-stop shop’ for the key chemical and biological data needed to contextualize industry precedents.” 

Also Read: Nomic and Broad Clinical Labs Partner to Integrate Omni 1000 into Discovery and Translational Workflows

The Molecule Search update introduces several connected capabilities:

• Parallel structure search across Drug Hunter Molecules, FDA-Approved Drugs, and Modern Clinical Compounds with 2D molecular property refinement
• Biological context for chemical matter, with disclosures indexed by drug target or pathogen and mechanism of action
• Referenceable compound & protein structural data within search results
• Visual landscape tools to overlay approved drugs, clinical compounds, and disclosures in chemical space, or isolate a dataset for deeper analysis

“Scientists rarely lack access to data. This is becoming even more of an issue as AI-based tools expand the net wider than ever. The problem is connecting it,” said John Overington, Ph.D., Chief Data Officer at Drug Hunter. “Molecule Search brings chemical structure and biological context into the same query, so researchers spend less time assembling a picture and more time understanding it.”

Source: PRNewswire

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