DeepMind Blog
May 16
Uncovering repurposed medicines to fight liver fibrosis
Stanford geneticist uses Co-Scientist to help find new treatments for chronic liver disease and liver fibrosis.
Uniting biological toolkits for a new approach to ALS
Co-Scientist unites Boston Children’s Hospital and MIT’s labs to explore new RNA-based treatments for ALS.
Related stories
arXiv Computer Vision
Sep 15
AlignUS: MRI-Guided Ultrasound Representation Learning for ALS Classification from Tongue Images
arXiv:2609.15285v1 Announce Type: new Abstract: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease in which early assessment remains challenging, particularly in low-resou...
OpenAI Blog
Aug 22, 2025
Accelerating life sciences research
Discover how a specialized AI model, GPT-4b micro, helped OpenAI and Retro Bio engineer more effective proteins for stem cell therapy and longevity research.
arXiv AI
Jun 9
The Montparnasse Algorithm for RNA Design
arXiv:2606. 07562v1 Announce Type: cross Abstract: RNA design consists of discovering a nucleotide sequence that optimizes predefined criteria, such as secondary structure.
arXiv AI
Sep 1
mRNA Design and Optimization with Deep Knowledge-Infused Approach
arXiv:2505.23862v2 Announce Type: replace-cross Abstract: The mRNA optimization is essential for mRNA vaccines, therapies, and industrial protein production. Based on current explorations, an ideal o...
DeepMind Blog
Nov 25, 2025
AlphaFold: Five years of impact
Explore how AlphaFold has accelerated science and fueled a global wave of biological discovery.
arXiv Computer Vision
Sep 21
Catena: A Comprehensive Software Suite for Large-Scale Connectomics
Catena is an open‑source, developer‑centric software suite designed to streamline large‑scale connectomics from electron microscopy data. It integrates modules for 3D neuron and organelle segmentation, synapse detection, microtubule tracking, and neurotransmitter inference into composable, chunk‑wise pipelines that are fully documented and extensible. The suite includes pretrained machine learning models, containerized runtimes, and shareable components to reduce compute and ground‑truth data needs while ensuring reproducible, scalable processing across workstations and clusters.
arXiv AI
Jul 21
A Benchmark for Early-stage Parkinson's Disease Detection from Speech
arXiv:2605. 14066v2 Announce Type: replace-cross Abstract: Early-stage Parkinson's disease (EarlyPD) detection from speech is clinically meaningful yet underexplored, and published results are hard to compare because studies differ in datasets, languages, tasks, evaluation protocols, and EarlyPD definitions.
arXiv AI
Jun 16
Unifying Acoustic Features and Text with Multimodal LLMs for Neurodegenerative Screening
arXiv:2606. 14788v1 Announce Type: cross Abstract: Voice-based screening offers a scalable and non-invasive way to assess neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD), but their staging remains challenging due to the difficulty of integrating heterogeneous data.
DeepMind Blog
May 16
Finding the molecular switches behind new infectious diseases
Clare Bryant uses Co-Scientist to identify genetic triggers in emerging infectious diseases.
arXiv AI
3d ago
Automatic estimation of verbal fluency index in people with Motor Neuron Disease using ASR alignment and pause modelling
The study introduces an automated system for estimating the Verbal Fluency Index (VFI) in individuals with Motor Neuron Disease (MND) by combining ASR (WhisperX) and VAD (Silero) with precise timestamping. Using a unique MND dataset, the approach outperformed traditional acoustic and self‑supervised embedding methods, achieving high predictive accuracy (R² up to 0.9 for P‑words and 0.8 for S‑words). Clinically inspired features were consistently superior, demonstrating the feasibility of automated VFI estimation for monitoring cognitive impairment in MND.
arXiv Machine Learning
Jul 28
SCTA: An Agentic Framework for Stable and Interpretable Target Gene Discovery from Single-Cell RNA Sequencing
arXiv:2607. 23821v1 Announce Type: new Abstract: Identifying therapeutic target genes from single-cell RNA sequencing (scRNA-seq) data remains a fundamental challenge in translational biology.