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.
OpenAI launches AI model GPT-Rosalind for life sciences research
OpenAI on Thursday introduced an artificial intelligence model touting increased biology knowledge and scientific research capabilities, as the startup deepens its push into the life sciences field.
Measuring AI’s capability to accelerate biological research in the wet lab
OpenAI introduces a real-world evaluation framework to measure how AI can accelerate biological research in the wet lab. Using GPT-5 to optimize a molecular cloning protocol, the work explores both the promise and risks of AI-assisted experimentation.
Biohub releases a world model of protein biology
Open AI models used for a wide range of scientific applications, including accelerating the design of therapeutic molecules.
Introducing GPT-Rosalind for life sciences research
OpenAI introduces GPT-Rosalind, a frontier reasoning model built to accelerate drug discovery, genomics analysis, protein reasoning, and scientific research workflows.
OpenAI debuts a life sciences AI model, entering crowd of tech giants selling to pharma
OpenAI became the latest tech giant to launch a life sciences-focused AI offering, aiming to build a biopharma business.
OpenAI launches new AI model for life sciences research
Researchers are drowning in data. AI could help.
Meet Biomni – an AI-powered biomedical co-scientist
In creating a comprehensive, AI-enabled research agent for the biomedical sciences, Stanford researchers hope to speed innovation by eliminating the tedium of scientific legwork.
Pause Giant AI Experiments: An Open Letter - Future of Life Institute
We call on all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4.
Accelerating scientific discovery with ChatGPT for Academic Researchers
OpenAI is giving 100,000 academic researchers free access to ChatGPT's most advanced AI models to accelerate scientific research, collaboration, and discovery.
mradermacher/GLM-4.6V-Flash-GGUF · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
No. 029: OpenAI math manuscripts, inquiry narrowing, virtual biology initiative - The Hallway Track
The Hallway Track No. 029 - How AI is affecting the practice of science
Early science acceleration experiments with GPT-5
AI models like GPT-5 are an increasingly valuable tool for scientists, but many remain unaware of the capabilities of frontier AI. We present a collection of short case studies in which GPT-5 produced new, concrete steps in ongoing research across mathematics, physics, astronomy, computer science, biology, and materials science. In these examples, the authors highlight how AI accelerated their work, and where it fell short; where expert time was saved, and where human input was still key. We document the interactions of the human authors with GPT-5, as guiding examples of fruitful collaboration with AI. Of note, this paper includes four new results in mathematics (carefully verified by the human authors), underscoring how GPT-5 can help human mathematicians settle previously unsolved problems. These contributions are modest in scope but profound in implication, given the rate at which frontier AI is progressing.
International, cross-sector collaboration commits nearly $2 billion to build foundational data for AI models to predict and treat disease
Biohub, DOE, NIH, and partners will generate open, standardized data to train AI models that predict how cells respond to interventions.
Kevin Weil 🇺🇸 on Twitter / X
💥 Today we’re introducing Prism—a free, AI-native workspace for scientists to write and collaborate on research, powered by GPT-5.2.Accelerating science requires progress on two fronts:1. Frontier AI models that use scientific tools and can tackle the hardest problems2.… pic.twitter.com/cnLysHixuQ— Kevin Weil 🇺🇸 (@kevinweil) January 27, 2026
#bioinformatics #computationalbiology #aiforscience #singlecell #spatialtranscriptomics #opensource #codex #claudecode #mcp #agenticai | Herry Z.
BioNexus: A Scientific Reliability Layer for Agentic Biology As coding agents such as OpenAI Codex, Claude Code, and Cursor become increasingly capable of running bioinformatics workflows, a new problem emerges: Knowing how to run an analysis does not mean knowing when that analysis is scientifically valid. BioNexus is an open-source project exploring a reliability layer for biological coding agents rather than another standalone AI scientist. Its current architecture focuses on: Machine-readable Capability Contracts — define input semantics, scientific preconditions, canonical backends, refusal conditions, and evidence requirements. EvidenceCard 2.1 — separates execution state from scientific evidence and grades conclusions as PRELIMINARY, SUPPORTED, ROBUST, REPLICATED, FRAGILE, or ABSTAIN. Scientific Intent & Invariant Routing — detects invalid analyses such as pseudoreplication, incompatible data representations, or missing backends. BioNexus Eval — evaluates routing, unsafe claims, refusal behavior, and scientific outcomes. RunBundle Artifact Contract — packages inputs, parameters, results, provenance, environment, and evidence into reproducible machine-readable runs. The long-term goal is simple: Make scientific reliability a first-class infrastructure layer for agentic biology. BioNexus is still evolving, and I am actively looking for contributors in bioinformatics, single-cell/spatial omics, scientific benchmarking, MCP, reproducibility, and AI coding agents. PRs, issues, benchmark datasets, scientific critiques, and failure cases are especially welcome. GitHub: HERRY423/BioNexus #Bioinformatics #ComputationalBiology #AIForScience #SingleCell #SpatialTranscriptomics #OpenSource #Codex #ClaudeCode #MCP #AgenticAI
Open Source for Science Fund Launches to Power AI-Driven Discovery
A new multi-donor fund by Renaissance Philanthropy seeded by Biohub and Wellcome opens its first call for proposals.
