Cleveland Clinic, RIKEN and IBM Team Advance to Finals for 2026 ACM Gordon Bell Prize

thequantuminsider.com Sep 9, 2026

Cleveland Clinic, RIKEN and IBM Team Advance to Finals for 2026 ACM Gordon Bell Prize

Curated by Shubham · TechCapsules · Sep 9, 2026

AI-summarised brief · reviewed before publication

Cleveland Clinic, RIKEN, and IBM researchers have been named finalists for the 2026 ACM Gordon Bell Prize for simulating biologically meaningful molecular systems containing up to 12,635 atoms. The team employed a quantum-centric supercomputing framework, integrating IBM Quantum Heron processors with the Fugaku, Miyabi-G, and ROQUO supercomputers. This hybrid approach calculated protein structures and binding energies, marking the largest molecules ever modeled with quantum computers. The methodology addresses key challenges in drug discovery by accurately computing atomic energies and modeling biological processes. By orchestrating CPUs, GPUs, and QPUs, the researchers eliminated complex manual data transfers and reduced errors. The work, initially published in May 2026, saw significant improvements in accuracy and scale within a year. The team validated their workflow on RIKEN’s newest system, demonstrating faster, more accessible research capabilities. The ACM Gordon Bell Prize recognizes outstanding achievement in high-performance computing. The 2026 winner will be announced at the SC26 conference in Chicago, November 15–20, 2026. This collaboration highlights the maturation of quantum computing in scientific applications.

💡 Why It Matters

· The integration of quantum processors with classical supercomputers eliminates manual data transfers, directly accelerating drug discovery workflows.
· This automation reduces computational overhead and errors, making complex molecular simulations more accessible for pharmaceutical research.

Read full article on thequantuminsider.com

Hear how AI can engineer nature’s comeback at TechCrunch Disrupt 2026

At TechCrunch Disrupt 2026, Ben Lamm, CEO of Colossal Biosciences, led a Real World AI Stage fireside chat on using artificial intelligence and synthetic biology to resurrect extinct species. The discussion examined the technical methods behind de‑extinction, the growing role of AI in modern biology, and the ethical debate over whether engineering nature serves as a conservation breakthrough or distracts from protecting existing ecosystems. The event highlighted how emerging tech is reshaping biodiversity science and policy.

💡 The conversation spotlights a pivotal shift where AI directly informs ecological restoration, challenging traditional conservation priorities and prompting urgent policy dialogue.

microsoft.com · Sep 14

How pharmaceutical leaders are operationalizing AI

Pharmaceutical leaders are moving beyond experimental AI pilots to embed artificial intelligence into core scientific, manufacturing, and operational workflows. This shift aims to accelerate drug development, which traditionally requires over a decade and billions of dollars, while addressing pressures for supply chain resilience and personalized care. Microsoft’s “AI for Better Health” initiative supports this transition by combining trusted data with human expertise. Key industry players are already operationalizing these tools to enhance decision-making speed and quality. Novo Nordisk reduced time-to-insight from weeks to minutes using a governed AI agent on Azure. Amgen deployed a Catalyst Copilot in six weeks to make institutional knowledge searchable. Almirall developed an assistant accessing over 400,000 documents, cutting retrieval times from days to seconds. These examples demonstrate that the focus has shifted from proving AI’s viability to scaling its enterprise-wide impact, enabling researchers to evaluate more hypotheses and improve patient outcomes through faster, data-driven innovation.

💡 The competitive divide in pharma is no longer defined by access to AI technology, but by the ability to integrate it into repeatable decision-making systems. Companies that successfully merge enterprise data with human expertise will drastically shorten discovery cycles, turning a decade-long process into a more agile, efficient operation.

psypost.org · Sep 14

How virtual reality is changing brain-training technology

A review in Applied Psychophysiology and Biofeedback evaluated 31 studies that combined EEG‑based neurofeedback with virtual‑reality (VR) environments. Researchers from the University of Graz found that VR feedback—such as making a flower bloom or a spaceship accelerate by altering brain waves—was generally preferred over traditional two‑dimensional displays, with participants reporting greater enjoyment, interest and perceived competence. The studies spanned healthy volunteers and clinical groups treating chronic pain, migraines, and post‑stroke symptoms, and employed varied tasks like navigating a ball through a forest or adjusting virtual lighting. Sample sizes ranged from single‑case reports to 100 participants, many under 50. While the review suggests VR can enhance engagement in brain‑training therapies, it also notes the need for more rigorous, larger‑scale trials to confirm specific benefits.

💡 Enhanced user engagement could accelerate the adoption of neurofeedback as a viable therapeutic tool for diverse neurological and pain conditions.

cyprus-mail.com · Sep 13

How AI could replace animal testing

Scientists across Europe are integrating artificial intelligence, human cell cultures and organ‑on‑a‑chip technologies to predict the toxicity of chemicals, aiming to replace the roughly 150,000 rats and mice used annually under the EU’s REACH regulation. The effort is coordinated by the €60 million ASPIS research cluster, which funds three initiatives, including Professor John Colbourne’s programme at the University of Birmingham and Professor Mathieu Vinken’s ONTOX project in Belgium. These projects target chronic toxicity testing, which is costly—up to €15 million per substance and several years—and often yields limited relevance to human health. By 2026 the teams hope to demonstrate AI‑driven, in‑vitro methods that meet safety requirements while adhering to the EU’s three‑Rs principle of replacement, reduction and refinement of animal use.

💡 Successful AI‑based testing could slash billions in compliance costs and accelerate market entry for new chemicals, reshaping the regulatory landscape for safety assessment.

northern-times.co.uk · Sep 13

Virtual reality lab training comes to Dornoch Academy

Pupils at Dornoch Academy participated in a week‑long workshop designed to teach National 5 Laboratory Science students the digital and practical competencies required for modern medicine manufacturing. Delivered by the Scotland RESILIENCE Hub—a specialist training centre at Heriot‑Watt University—and coordinated by teacher Dr Adrian Allan and Highland Council STEM Improvement Officer Katie Elder, the program combined hands‑on aseptic technique drills, precise measurement exercises, and assembly of bioreactor equipment with immersive extended‑reality (XR) simulations. The session aimed to support students’ Candidate Review coursework for the Careers Unit, which outlines real‑world science careers and employer expectations. Teachers praised the pupils’ teamwork, adaptability and technical precision as they moved between virtual labs and physical bench work, reflecting the skill set needed to address the UK’s shortage of qualified life‑science technicians.

💡 The training directly equips a pipeline of students with the exact competencies employers lack, accelerating the closure of the life‑science skills gap.

9to5mac.com · Sep 12

Apple researchers unveil SimpleDesign, a new AI model for protein design

Apple researchers have introduced SimpleDesign, a streamlined artificial intelligence model capable of jointly generating protein sequences and their corresponding three-dimensional structures. Building upon their previous SimpleFold study, which utilized flow-matching for structure prediction, this new model addresses the broader challenge of protein design. Unlike existing systems that rely on complex, multi-stage training processes involving autoencoders and latent space representations, SimpleDesign operates directly in the data space. It employs an end-to-end training approach using general-purpose Transformer blocks, eliminating the need for intermediate tokenization steps. The model was trained on over two million protein sequence-and-structure pairs from the AFESM dataset. During training, researchers corrupted both sequences and structures with varying degrees of noise, allowing the system to learn folding, inverse folding, and co-design tasks simultaneously. Benchmarks indicate that SimpleDesign delivers competitive performance in structure and sequence generation, producing plausible proteins with sequences that match or exceed those of most competing multimodal models, despite its significantly simpler architecture.

💡 By eliminating complex multi-stage training pipelines, Apple demonstrates that high-performance protein design does not require computationally expensive intermediate representations. This architectural simplification lowers the barrier to entry for biological research, allowing scientists to generate viable protein candidates with greater efficiency and less infrastructure overhead than previously possible.