SAIR: Accelerating Pharma R&D with AI-Powered Structural Intelligence
SandboxAQ has published a blog post on Hugging Face describing SAIR (Structural AI for Research), a system applying AI to structural biology data for drug discovery acceleration. The post outlines how structural intelligence—likely leveraging protein structure prediction or molecular modeling—is being applied to pharmaceutical R&D pipelines. This represents an enterprise deployment of AI in the life sciences domain, combining structural biology with machine learning.
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SIA: Self-Improving AI framework for autonomous benchmark performance improvement
SIA (Self Improving AI) is an open-source Python framework from hexo-ai designed to autonomously improve the performance of AI models or agents on benchmark tasks. The repository is trending on GitHub with 1,228 total stars and 177 new stars today. The framework targets a core challenge in AI development: automated self-improvement loops without human intervention.
Building Deep Research: How Tavily Achieved State of the Art in AI Research Agents
Tavily published a technical blog post on Hugging Face describing how they built their Deep Research system, claiming state-of-the-art performance. The post covers the architecture and methodology behind their AI-powered deep research agent. As a tier-2 source, this represents a practitioner-level account of building agentic research pipelines using web search and retrieval tooling.
Anthropic launches AI for Science program offering free API credits to researchers
Anthropic is launching an AI for Science program that provides free API credits to qualified researchers at academic institutions, with a focus on biology, life sciences, drug discovery, and agricultural productivity. Researchers are selected based on scientific contribution, potential impact, and AI's ability to accelerate their work. The initiative aligns with Dario Amodei's 'Machines of Loving Grace' vision and represents a structured philanthropic/access program rather than a technical release.
Comments on U.S. National AI Research Resource Interim Report
Hugging Face published commentary on the U.S. National AI Research Resource (NAIRR) interim report, which outlines a proposed federal initiative to provide researchers with shared access to compute, data, and other AI infrastructure. The post likely advocates for open-source and open-science principles in shaping the NAIRR's design. This represents an industry stakeholder weighing in on a significant U.S. AI policy and infrastructure initiative.
AiraXiv: AI-Driven Open-Access Publishing Platform for Human and AI Scientists
AiraXiv is a proposed open-access academic publishing platform designed to accommodate both human and AI-generated research outputs, addressing scalability challenges in traditional peer review. The platform supports AI scientists via Model Context Protocol (MCP)-based interactions and human scientists through an interactive UI, with papers evolving through continuous feedback-driven iteration. It was validated through real-world deployment as the submission platform for ICAIS 2025. The work positions itself as infrastructure for a future where AI agents are first-class participants in the scientific publishing ecosystem.
Accelerating discovery of liver disease mechanisms with Co-Scientist
DeepMind's Co-Scientist AI system is being used by researcher Filippo Menolascina to identify new treatment mechanisms for liver disease and explain differential drug response across patients. The application demonstrates Co-Scientist's utility in biomedical hypothesis generation and drug discovery workflows. This represents a concrete scientific use case for AI-assisted research in a clinical domain.
Open-source DeepResearch – Freeing our search agents
Hugging Face published a blog post introducing Open Deep Research, an open-source replication of agentic deep research capabilities (similar to OpenAI's Deep Research). The project aims to build open-weight search agents capable of multi-step web research and synthesis. The post details the architecture, tooling, and early benchmark results of the system.
Eli Lilly Commits Up to $2.75 Billion to Insilico Medicine for AI-Driven Drug Discovery
Eli Lilly agreed to pay up to $2.75 billion to Insilico Medicine, a Hong Kong biotech using generative AI across its drug-discovery pipeline, with an initial $115 million for exclusive rights to undisclosed pre-clinical drug candidates. Insilico's platform uses PandaOmics for target identification and Chemistry42 for molecule design, reducing the time from target identification to preclinical candidates from 5-6 years to roughly 18 months and screening far fewer compounds than conventional methods. The deal is the third between the companies and follows positive Phase 2a results for Rentosertib, an AI-discovered drug targeting idiopathic pulmonary fibrosis. No AI-discovered drug has yet received regulatory approval, and the key open question is whether AI-accelerated compounds will show higher clinical trial success rates than traditionally developed drugs.