4MIT Technology Review — AI·
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.
Related guides (2)
Related events (8)
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 traces the path from current AI agents to artificial superintelligence
MIT Technology Review publishes a commentary piece exploring the trajectory from today's narrow AI agents toward artificial superintelligence, using a multi-agent healthcare scenario to illustrate current coordination limitations. The piece argues that today's agents can exchange data but lack true coordination, framing this gap as a key obstacle on the path to more capable systems. The article represents mainstream technology press engaging with ASI timelines and multi-agent architectures.
MIT Technology Review: Agentic AI as a solution to global health care strain
MIT Technology Review publishes a commentary arguing that agentic AI could help address systemic pressures in global health care, including chronic underinvestment, staff burnout, and fragmented access to care. The piece frames agentic AI as a potential tool for 'rehumanizing' care delivery rather than replacing human workers. The article is a high-level industry analysis piece without specific technical claims or product announcements.
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.
Preparing for future AI risks in biology
OpenAI has published a post outlining its proactive approach to assessing and mitigating biosecurity risks from advanced AI systems capable of biological applications. The piece describes capability evaluations and safeguards designed to prevent misuse of AI in biology and medicine. This reflects OpenAI's ongoing effort to get ahead of dual-use risks before capabilities reach dangerous thresholds.
MIT Technology Review: Advanced materials science as a foundation for next-gen AI infrastructure
MIT Technology Review examines how advanced materials science underpins AI hardware progress, arguing that improvements in processing power, memory, and energy efficiency depend on materials innovation below the semiconductor and data center layer. The piece frames materials R&D as a critical but underappreciated enabler of AI scaling. The article is a commentary piece rather than a primary research or product announcement.
MIT Technology Review outlines enterprise infrastructure requirements for agentic AI
MIT Technology Review publishes a commentary piece on the infrastructure and platform requirements for deploying agentic AI in enterprise settings, covering CPU capacity, data access, policy-aware tool use, observability, and memory management. The piece frames agentic AI as end-to-end business task execution across people, workflows, data, and systems rather than a chatbot upgrade. It reflects growing industry attention to the operational and architectural prerequisites for enterprise agent deployment.
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.