5Latent Space (swyx)·
Xaira Therapeutics on causal AI models and X-Cell for drug discovery
Latent Space interviews Bo Wang (Chief Discovery Officer) and Ci Chu (Chief AI Scientist) at Xaira Therapeutics about their X-Cell model and their strategy of generating proprietary data to train causal AI models for drug discovery. The discussion centers on why causal models require causal data and how Xaira is investing heavily in data generation as a foundation for model building. This is a substantive look at how a well-funded biotech is operationalizing AI-native drug discovery at scale.
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Lila Sciences bets on scientific data as the last frontier for AI training
Latent Space interviews Andy Beam and Rafa Gómez-Bombarelli of Lila Sciences, a lab building robotic scientific infrastructure to generate novel training data for AI. The core thesis is that scientific experimentation—not internet text—is the next major untapped data source for frontier AI. The piece covers what this looks like operationally, including a room full of robots conducting experiments.
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.
MIT Technology Review examines AI-driven drug discovery and the data loop challenge
MIT Technology Review publishes an analysis of AI applications in pharmaceutical drug discovery, framing the challenge around Eroom's Law — the observation that drug development costs have roughly doubled every nine years since the 1950s. The piece focuses on how closing feedback loops in AI-driven discovery pipelines could address the 10-15 year, high-cost development cycle. The article represents industry-level commentary on AI deployment in life sciences rather than a specific technical or product announcement.
MIT Technology Review surveys AI-driven drug design for next-generation biologic medicines
MIT Technology Review examines how AI is being applied to the design of biologic medicines — protein-based therapies — where traditional drug development is expensive and failure-prone. The piece covers the use of AI to accelerate candidate identification and reduce attrition in the development pipeline. This is a high-level survey of an active application domain rather than a specific model or tool release.
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.
Anthropic launches AI for Science rare disease research grants with up to $50K in Claude credits
Anthropic is opening applications for a focused grant program within its AI for Science initiative, targeting rare genetic disease research with up to $50,000 in Claude API credits per recipient over six months. The program has two tracks: one for basic science researchers and one for early-stage biotechs accelerating drug development. Anthropic is partnering with the Monarch Initiative, an international rare disease consortium, and highlights use cases including mechanistic disease classification, regulatory documentation drafting, and therapeutic target analysis. The initiative reflects Anthropic's strategy of directing frontier model access toward high-impact scientific domains.
Finding the molecular switches behind new infectious diseases
DeepMind's Co-Scientist AI tool is being used by researcher Clare Bryant to identify genetic triggers in emerging infectious diseases. The application demonstrates Co-Scientist's utility in accelerating biological discovery, specifically in understanding molecular mechanisms underlying new pathogens. This represents a concrete scientific use case for AI-assisted research in infectious disease biology.
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.