Open AI models used for a wide range of scientific applications, including accelerating the design of therapeutic molecules.
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.
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.
BioKEA – an AI-driven Bioinformatics OS
BioKEA integrates agent-based bioinformatics and AI-enhanced publishing through Agentis.
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 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.
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.
Introducing LM Studio Bionic: the AI agent for open models
The AI agent made for open models, built to get things done.
OpenAI launches new AI model for life sciences research
Researchers are drowning in data. AI could help.
Google figures out how to watermark AI-designed proteins
Intended to help with biosecurity, it works with a popular AI protein design tool.
Agentic systems are adept at solving well-scoped, verifiable problems in computational biology
We introduce CompBioBench, a benchmark of 100 diverse tasks for evaluating agentic systems in computational biology. Unlike mathematics and programming, which more readily admit systematic verification, biological data are inherently noisy and open to interpretation.
#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
AI for Drug Discovery Needs Trustworthy Biological Data
To unleash the potential of AI in life sciences and drug discovery, biological datasets must be authenticated, validated, and supported by standardized metadata.
DeepMind releases structure predictions for nearly every known protein
Database powered by AlphaFold algorithm now boasts predicted structures for over 200 million proteins
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.
How AI Can Advance Mathematical Biology: Opportunities, Challenges, and Future Directions
Mathematical biology has long relied on mechanistic models, including ordinary and partial differential equations, stochastic systems, and agent-based models, to study biological processes across scales. These approaches remain central because they provide structure, interpretability, and biological insight. However, modern biological and biomedical data, including longitudinal clinical records, medical imaging, and multi-omics measurements, are often high-dimensional, noisy, heterogeneous, and incomplete. These features make model calibration, simulation, and uncertainty quantification increasingly difficult. As a result, artificial intelligence (AI) and machine learning (ML) are playing a growing role in mathematical biology, not as replacements for mechanistic modeling, but as complementary tools that can support prediction, hybrid modeling, inference, and control. In this review, we organize these uses of AI from a mathematical biology perspective and examine how data-driven and mechanistic approaches interact across different modeling tasks. The selected examples span biomedical, epidemiological, ecological, evolutionary, biochemical, and population-level systems, while the review is intended to be representative rather than exhaustive. We emphasize recurring challenges that strongly affect biological credibility and practical usefulness, including interpretability, identifiability, generalization, and uncertainty quantification. Our goal is to clarify the roles AI can play in mathematical biology and to highlight the opportunities and limitations that arise when flexible learning methods are integrated with biologically grounded modeling.
