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UCB advances its digital antibody engineering program with new Cradle collaboration

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Collaboration adds Cradle’s end-to-end platform to UCB’s computational capabilities, supporting faster discovery of complex therapies for severe diseases

BRUSSELS and AMSTERDAM, Nov. 4, 2026 /PRNewswire/ — UCB, a global biopharmaceutical company, and Cradle, which builds a leading AI-driven protein design and engineering platform, today announced a strategic collaboration. The collaboration is the latest step in UCB’s expanding digital antibody discovery and engineering program, which draws on the company’s own successful antibody discovery platform combined with computational science, deep expertise in disease biology and a network of technology and research partners.

Under the collaboration, UCB will apply Cradle’s platform within selected discovery projects from hit generation through lead optimization and preclinical readiness. The collaboration reflects UCB’s mission of translating cutting-edge science into meaningful advances for patients. By fully integrating AI across the discovery process and strengthening its digital antibody engineering capabilities, UCB aims to accelerate the ability to identify and optimize high-quality candidates for diseases where patients continue to need better options.

“At UCB, we view AI not as something to chase for its own sake, but as a scientific force multiplier that augments human expertise across biology, chemistry, and translational research. We are excited to partner with Cradle as we expand our digital antibody engineering capabilities,” said Alistair Henry, Chief Scientific Officer of UCB. “Adding Cradle’s end-to-end platform across our discovery pipeline allows us to apply computational approaches in a deeply rigorous and translational way. Predictive modeling combined with wet-lab validation allows us to potentially design better molecules, learn faster, and bring high-quality therapeutics to patients even sooner.”

As UCB scientists use the Cradle platform across programs, experimental results continuously feed back into program-specific models. This creates a compounding learning loop or “lab-in-the-loop” that supports scientists in applying their expertise and judgement to optimize candidates across the full set of properties required for success and accelerate progression toward a potential candidate.

“AI will have its greatest impact on biologics R&D when it can be scaled beyond individual experiments or programs and become part of how scientists work across the discovery organization,” said Stef van Grieken, CEO and Co-Founder of Cradle. “Partnering with UCB brings Cradle across its biotherapeutics pipeline, giving scientists a common platform to apply AI to more programs, more modalities and more complex engineering challenges and ultimately advance better therapies.”

About UCB
UCB, Brussels, Belgium (www.ucb.com) is a global biopharmaceutical company focused on the discovery and development of innovative medicines and solutions to transform the lives of people living with severe diseases of the immune system or of the central nervous system. With more than 9000 people in approximately 40 countries, the company generated revenue of €7.7 billion in 2025. UCB is listed on Euronext Brussels (symbol: UCB).

About Cradle
Cradle’s mission is to enable every scientist to turn today’s impossible cures, foods, and materials into tomorrow’s ordinary. Cradle’s end-to-end protein engineering platform helps scientists intentionally engineer biologics across the full design-to-candidate lifecycle, bringing AI-driven molecular engineering, active learning from experimental data, and multi-property optimization together in a single system. Today, the platform is trusted by many of the biggest names in global pharmaceutical, industrial bio, agriculture and consumer goods across more than 80 active R&D programs. Cradle is based in Amsterdam, The Netherlands; Zürich, Switzerland and the United States with a team of machine learning and biotech research experts with deep industry experience from many of the world’s leading technology and biotech companies. Cradle is backed by IVP as well as Index Ventures and Kindred Capital. For more information, visit cradle.bio.

Forward-looking statements
This document contains forward-looking statements, including, without limitation, statements containing the words “potential”, “believes”, “anticipates”, “expects”, “intends”, “plans”, “seeks”, “estimates”, “may”, “will”, “continue” and similar expressions. These forward-looking statements are based on current plans, estimates and beliefs of management. All statements, other than statements of historical facts, are statements that could be deemed forward-looking statements, including estimates of revenues, operating margins, capital expenditures, cash, other financial information, expected legal, arbitration, political, regulatory or clinical results or practices and other such estimates and results. By their nature, such forward-looking statements are not guaranteeing future performance and are subject to known and unknown risks, uncertainties, and assumptions which might cause the actual results, financial condition, performance or achievements of UCB, or industry results, to be materially different from any future results, performance, or achievements expressed or implied by such forward-looking statements contained in this document.

Important factors that could result in such differences include but are not limited to: global spread and impacts of wars, pandemics and terrorism, the general geopolitical environment, climate change, changes in general economic, business and competitive conditions, the inability to obtain necessary regulatory approvals or to obtain them on acceptable terms or within expected timing, costs associated with research and development, changes in the prospects for products in the pipeline or under development by UCB, effects of future judicial decisions or governmental investigations, safety, quality, data integrity or manufacturing issues, supply chain disruption and business continuity risks; potential or actual data security and data privacy breaches, or disruptions of our information technology systems, product liability claims, challenges to patent protection for products or product candidates, competition from other products including biosimilars or disruptive technologies/business models, changes in laws or regulations, exchange rate fluctuations, changes or uncertainties in tax laws or the administration of such laws, and hiring, retention and compliance of its employees. There is no guarantee that new product candidates will be discovered or identified in the pipeline, or that new indications for existing products will be developed and approved. Movement from concept to commercial product is uncertain; preclinical results do not guarantee safety and efficacy of product candidates in humans. So far, the complexity of the human body cannot be reproduced in computer models, cell culture systems or animal models. The length of the timing to complete clinical trials and to get regulatory approval for product marketing has varied in the past and UCB expects similar unpredictability going forward. Products or potential products which are the subject of partnerships, joint ventures or licensing collaborations may be subject to disputes between the partners or may prove to be not as safe, effective or commercially successful as UCB may have believed at the start of such partnership. UCB’s efforts to acquire other products or companies and to integrate the operations of such acquired companies may not be as successful as UCB may have believed at the moment of acquisition. Also, UCB or others could discover safety, side effects or manufacturing problems with its products and/or devices after they are marketed. The discovery of significant problems with a product similar to one of UCB’s products that implicate an entire class of products may have a material adverse effect on sales of the entire class of affected products. Moreover, sales may be impacted by international and domestic trends toward managed care and health care cost containment, including pricing pressure, political and public scrutiny, customer and prescriber patterns or practices, and the reimbursement policies imposed by third-party payers as well as legislation affecting biopharmaceutical pricing and reimbursement activities and outcomes. Finally, a breakdown, cyberattack or information security breach could compromise the confidentiality, integrity and availability of UCB’s data and systems.

Given these uncertainties, the public is cautioned not to place any undue reliance on such forward-looking statements. These forward-looking statements are made only as of the date of this document, and do not reflect any potential impacts from the evolving event or risk as mentioned above as well as any other adversity, unless indicated otherwise. The company continues to follow the development diligently to assess the financial significance of these events, as the case may be, to UCB.

UCB expressly disclaims any obligation to update any forward-looking statements in this document, either to confirm the actual results or to report or reflect any change in its forward-looking statements with regard thereto or any change in events, conditions or circumstances on which any such statement is based, unless such statement is required pursuant to applicable laws and regulations.

 

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SOURCE Cradle; UCB

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Deep Principle Introduces Hydrogen-1.0 for AI-Driven Materials Discovery and Chemistry Research

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17 minutes ago

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November 6, 2026

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LOS ANGELES, Nov. 6, 2026 /PRNewswire/ — On November 4, 2026, Deep Principle introduced Hydrogen-1.0, an open-weight scientific model that helps chemistry and materials research teams plan experiments, run computational workflows and explore candidates for desired material properties. Researchers can use Hydrogen-1.0 through Mira, Deep Principle’s AI Scientist, as well as model downloads and an API.

Built by post-training GLM-5.3, Hydrogen-1.0 draws on chemistry, materials science and life sciences data, together with records and agent trajectories from Deep Principle’s own laboratories. Supervised fine-tuning provides examples of reasoning, experimental protocols and results analysis; reinforcement learning uses feedback on research outcomes to improve decisions. Training preserves hypotheses, actions, observations and revisions, including failed attempts.

From chemistry research goals to laboratory protocols

For AI-assisted experiment design, Hydrogen-1.0 turns a brief research goal into an experimental plan, generates precise protocols and interprets results. It learns from Deep Principle’s electronic laboratory notebook records, supported by Mira’s agentic documentation, which connect experimental intent, procedures, observations and outcomes. In the company’s evaluation across shared experiment-planning questions, Hydrogen-1.0 achieved a higher mean rubric score than Kimi-K3.

A laboratory compilation example shows how Hydrogen-1.0 prepares instructions for particular equipment. In a 12-vial Suzuki coupling example, Hydrogen-1.0 wrote a JSON protocol and tested it in a virtual rehearsal. The rehearsal detected an attempt to aspirate without a fresh tip. Hydrogen-1.0 corrected the action order and successfully compiled the revised protocol before final execution and analysis handoff. The checks covered station capabilities, dependencies and action prerequisites.

Running simulations and exploring reaction pathways

In a polymer molecular dynamics example, Hydrogen-1.0 translated a materials question into an executable simulation workflow. It wrote Python to build a polyethylene melt, ran GROMACS, detected and repaired a topology error, and restarted the simulation. It then examined the corrected 40-nanosecond trajectory, including density, molecular packing, diffusion and chain dimensions.

For reaction pathway exploration, Hydrogen-1.0 generated 27 molecular structures through 30 plausible reactions in an Ireland–Claisen rearrangement network. The network includes competing branches and return paths around a five-step sequence of Cope and Claisen rearrangements and silyl shifts. In the selected comparison, the main route had a lower electronic barrier than its displayed alternative at each step. Mapping these possibilities helps researchers compare routes, identify potential bottlenecks and prioritize further investigation.

Selecting materials through repeated evaluation

Hydrogen-1.0 approaches materials discovery as a sequence of candidate choices and evaluations. Deep Principle trains the model on complete discovery trajectories that connect a desired property to candidate selection, synthesis strategies, evaluation and the next research decision. This gives the model experience in choosing what to investigate and using the resulting evidence to guide further exploration.

In a transition metal complex design evaluation, Hydrogen-1.0 recommended new complexes over five rounds, seeking a larger HOMO–LUMO gap. All models began with the same initial population. Each received only the results of its own recommendations as feedback for the next round, maintaining independent evaluation histories. By round five, the mean best-so-far gap reached 4.318 eV for Hydrogen-1.0, compared with 3.689 eV for GPT-6-Astra and 3.353 eV for Kimi-K3.

The result points to the potential of learning from discovery trajectories to help researchers find materials with desired properties in fewer attempts. Deep Principle’s next research focus is to extend learning across discovery targets and material families, so that experience from one campaign can improve the model’s approach to the next.

Scientific evaluations and access

In Deep Principle’s evaluations, Hydrogen-1.0 outperforms GPT-6-Astra on FrontierScience-Research and improves on GLM-5.3 on GPQA Diamond, roughly matching Kimi-K3 on the latter benchmark. In internal scientific computing and one-step forward reaction prediction tasks, Hydrogen-1.0 scored higher than GPT-6-Astra running at each of its low, high and maximum thinking settings. Against Astra at maximum thinking, its leads were 5.13 and 4.76 percentage points, respectively. Astra at maximum thinking scored 3.33 percentage points higher on NMR structure elucidation.

Deep Principle offers four routes to Hydrogen-1.0: open-weight downloads on Hugging Face and ModelScope, direct API access on NeuroSpark, and use within Mira.

Deep Principle worked with UniPat and Mind Lab on data, the training pipeline and infrastructure, and with NeuroSpark on training infrastructure. The company welcomes collaboration with the open-model community to develop scientific models and advance scientific discovery.

About Deep Principle

Deep Principle builds intelligence for scientific discovery in chemistry, materials science and life sciences. Its work connects scientific models, laboratory workflows and materials discovery. Mira is Deep Principle’s AI Scientist.

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SOURCE Deep Principle

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Cavli Wireless to Unveil P16QS LTE Cat 1bis Module and Cortex AI Coding Assistant at electronica 2026

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18 minutes ago

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November 6, 2026

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MUNICH, Nov. 6, 2026 /PRNewswire/ — Cavli Wireless, a leading end-to-end IoT solutions provider, will unveil the P16QS LTE Cat 1bis System-on-Module (SoM) and Cortex AI, its autonomous firmware engineering assistant, at electronica 2026, taking place from November 10–13, in Munich, Germany. Visit Cavli Wireless at Hall C2, Booth 628, to explore our 5G module portfolio, including 5G RedCap and 5G NR solutions, our LTE Cat 1bis module portfolio, and the Cavli Hubble IoT connectivity and device management platform.

The showcase reinforces Cavli’s commitment to simplifying IoT development and deployment through integrated cellular hardware, connectivity, cloud-based device management, and AI-powered firmware engineering.

P16QS LTE Cat 1bis Module: Enabling Smart, Connected IoT Devices

The Cavli P16QS is a compact LTE Cat 1bis SoM designed for a wide range of connected applications, including payment terminals, connected soundboxes, cost-effective instrument clusters, vehicle telematics, asset and mobility tracking, and secure IoT devices. It combines cellular connectivity, local wireless communications, application processing, and hardware-backed security. Powered by a 32-bit RISC-V processor running at 325 MHz, the module supports RTOS-based application development and gives OEMs a foundation for connected-device designs.

Key features of the P16QS include:

LTE Cat 1bis connectivity with peak download speeds of 10 Mbps and upload speeds of 5 MbpsA 32-bit RISC-V processor operating at 325 MHz with integrated application processingWi-Fi 6 and Bluetooth Low Energy 5.4 for local wireless connectivityAn integrated secure element and interfaces for NFC, smart cards, and magnetic-stripe readers for payment-oriented designsOptional multi-constellation GNSS and optional integrated eSIM, with connectivity and device management through Cavli HubbleA compact 40.0 × 30.0 × 2.7 mm LGA package with peripheral and display interfaces

At electronica 2026, the P16QS will be featured in a Point-of-Sale (POS) solution concept, demonstrating how payment peripherals, local processing, secure connectivity, and wireless communications can be integrated into a single module-based design. Cavli will also showcase its other LTE Cat 1bis solutions, including the CQ16 and C16QS modules.

Explore P16QS: https://www.cavliwireless.com/iot-modules/c-series/p16qs

Cortex AI: Connecting Firmware Development Directly to Hardware

Cavli will unveil Cortex AI, an autonomous firmware engineering assistant developed to streamline application development for IoT-enabled solutions. Integrating programming tools, technical resources, and hardware testing into a unified workflow, Cortex AI automates key development stages, including code generation, compilation, flashing, and device boot-up. This streamlines firmware validation, reduces repetitive manual tasks, and helps engineering teams accelerate development cycles. Depending on project complexity and deployment requirements, Cortex AI aims to shorten development cycles traditionally measured in months to weeks.

5G at the Edge: CQM220 RedCap and CQM211 5G NR Router Prototypes

Cavli Wireless offers a comprehensive 5G portfolio spanning 5G RedCap, 5G NR, and automotive-grade modules for industrial, enterprise, and automotive IoT applications. The portfolio addresses mid-tier deployments requiring balanced performance, power efficiency, and cost, alongside bandwidth-intensive applications demanding multi-gigabit connectivity and automotive use cases with stringent manufacturing standards. At electronica 2026, Cavli will showcase router prototypes powered by its CQM220 5G RedCap and CQM211 5G NR modules, available in LGA and M.2 form factors to support diverse integration needs.

CQM220 5G RedCap Module

Built on 3GPP Release 17 and powered by the Qualcomm SDX35 chipset, the CQM220 is designed for efficient 5G connectivity in mid-tier IoT applications. It combines 5G RedCap with LTE Cat 4 fallback, integrated processing, dual-band GNSS and OpenWrt support, making it suitable for industrial gateways, smart infrastructure, healthcare devices and connected logistics systems.

Key features of the CQM220 include:

5G RedCap connectivity with peak download speeds of 220 Mbps and upload speeds of 120 MbpsLTE Cat 4 fallback with peak download speeds of 150 Mbps and upload speeds of 50 MbpsQualcomm SDX35 chipset with an Arm Cortex-A7 processor operating at up to 1.9 GHzOpenWrt support, PCIe Gen2, and USB 2.0 connectivityIntegrated dual-band GNSS for location-aware IoT applicationsLGA and M.2 form-factor options

CQM211 5G NR Module

The CQM211 is a high-performance 5G NR module for bandwidth-intensive IoT and machine-to-machine applications. Based on 3GPP Release 16 and powered by the Qualcomm SDX61 chipset, it supports multi-gigabit connectivity, LTE Cat 16 fallback, carrier aggregation and Yocto Linux. The module is suited for a wide range of applications, including industrial routers, broadband gateways, smart surveillance systems, and high-performance edge computing devices.

Key features of the CQM211 include:

5G NR peak download speeds of up to 2.5 Gbps in NSA mode and 1.5 Gbps in SA modeLTE Cat 16 fallback for connectivity across supported LTE networksQualcomm SDX61 chipset with Arm Cortex-A7 processing and Hexagon DSP capabilitiesSupport for 5G and LTE carrier aggregationYocto Linux support, PCIe Gen3, and USB 3.1/2.0 connectivityLGA and M.2 form-factor options

Visitors can see both modules in router prototypes and explore how their respective performance capabilities and interfaces align with different device architectures and application requirements.

Cavli Hubble: Visibility and Control Beyond Deployment

Cavli Hubble, Cavli Wireless’ unified IoT connectivity and device management platform, will be demonstrated live. The cloud-based environment brings together global cellular connectivity, eSIM lifecycle management, device monitoring, remote diagnostics and firmware management.

The demonstration will feature Hubble Lens for remote AT-command access and diagnostics, FOTA/DFOTA for remote firmware updates, and Hubble Cloud Connect for device-to-cloud data routing. These capabilities help OEMs and enterprises manage distributed IoT fleets, maintain operational visibility and reduce reliance on on-site intervention. Customer devices powered by Cavli modules will also be displayed to highlight real-world IoT applications.

“Building an IoT product should not automatically imply months lost in integrating hardware, connectivity, firmware tools and cloud operations together,” said John Mathew, Founder and Chief Executive Officer of Cavli Wireless

“With the purposefully designed P16QS, Cavli’s AI Coding Assistant for hassle-free firmware development, 5G modules and Hubble cloud platform, our goal is to create an effortless connected path from development to deployment.”

Cavli360: A Conversation Across the IoT Ecosystem

Visitors attending electronica 2026 in Munich can collect printed copies of Cavli360 IoT Magazine – Edition 1 from the Cavli Wireless booth (Hall C2, Booth 628) or access the digital edition by scanning the QR code available at the booth. Featuring insights from Cavli experts, customers, partners, distributors, and industry analysts, the inaugural edition explores 5G, AI-driven IoT, the evolution toward 6G, regional market trends, and industry collaboration.

Explore or request the magazine: https://www.cavliwireless.com/cavli360/edition-1.

About Cavli Wireless

Cavli Wireless is a global end-to-end IoT solutions provider enabling organizations to build, deploy, and manage connected products at scale. Cavli’s integrated hardware-to-cloud ecosystem combines cellular IoT modules with built-in eSIM connectivity and Cavli Hubble™, a cloud-based platform for IoT connectivity, device and SIM lifecycle management. With cellular modules designed, engineered and manufactured in India for a global customer base, Cavli combines integrated eSIM connectivity and Cavli Hubble device management to help businesses develop, connect and manage intelligent products. The company offers customized solutions supported by 24/7 global assistance.

About electronica 2026

electronica 2026 takes place November 10–13 at Trade Fair Center Messe München in Munich, Germany. The biennial international trade fair brings together technology manufacturers, component suppliers, developers, and industry professionals to explore advances in electronics, semiconductors, embedded systems, connectivity, automation, and emerging technologies.

Soorya Kumar, Senior Associate – Product Marketing
Cavli Wireless
soorya.kumar@cavliwireless.com
+91 6282714144
cavliwireless.com

 

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Analog Devices Completes Acquisition of Alif Semiconductor

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18 minutes ago

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November 6, 2026

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TAIPEI, Nov. 6, 2026 /PRNewswire/ — Analog Devices, Inc. (NASDAQ: ADI) today announced the completion of its acquisition of Alif Semiconductor. The combination of ADI’s leadership across sensing, signal processing, power, connectivity and application software, with Alif’s AI-native microcontrollers and fusion processors, expands ADI’s total addressable market and accelerates innovation across industrial, data center infrastructure, defense, energy, robotics, digital health and wearable applications.

Commentary

“We are excited to welcome the Alif team to ADI and begin realizing the potential of bringing our technologies together,” said Vincent Roche, CEO and Chair of ADI. “For decades, ADI has helped customers solve their most complex system-level challenges through deep domain expertise and a cutting-edge technology portfolio spanning the physical-digital boundary. Alif’s AI-native platform builds on that foundation, advancing systems that can understand real-world signals, reason and act locally within demanding power, latency, security and reliability constraints. This is physical intelligence in action.”

About Analog Devices, Inc.

Analog Devices, Inc. (NASDAQ: ADI) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, AI, and software technologies into solutions that combat climate change, reliably connect humans and the world, and help drive advancements in automation and robotics, mobility, healthcare, energy and data centers. With revenue of more than $11 billion in FY25, ADI ensures today’s innovators stay Ahead of What’s Possible. Learn more at www.analog.com and on LinkedIn and X.

Forward-Looking Statements

This press release contains forward-looking statements, which address a variety of subjects including, for example, the expected benefits of the transaction; ADI’s expected product offerings and benefits of those offerings, product development, and technical advances resulting from the transaction; markets, market position, addressable markets, and growth opportunities; and other future events. Statements that are not historical facts, including statements about our beliefs, plans, and expectations, are forward-looking statements. Such statements are based on our current expectations and are subject to a number of factors and uncertainties, which could cause actual results to differ materially from those described in the forward-looking statements. The following important factors and uncertainties, among others, could cause actual results to differ materially from those described in these forward-looking statements: unforeseen or unknown liabilities; costs or expenses related to the transaction; the inability to retain key personnel; difficulties in integrating the acquired business; the risk that expected benefits of the transaction may not be realized or may take longer to realize than expected; and uncertainty as to the long-term value of our common stock. For additional information about factors that could cause actual results to differ materially from those described in the forward-looking statements, please refer to our filings with the Securities and Exchange Commission, including the risk factors contained in our most recent Annual Report on Form 10-K and Quarterly Report on Form 10-Q. Forward-looking statements represent management’s current expectations and are inherently uncertain. Except as required by law, we do not undertake any obligation to update forward-looking statements made by us to reflect subsequent events or circumstances.

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SOURCE Analog Devices, Inc.

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