AI in Healthcare

Today's Stories

1. 1

   Anthropic launches drug discovery programs for neglected diseases

   Anthropic announced it is launching its own drug discovery programs targeting neglected diseases that traditional pharma and biotech firms consider unprofitable. The company will focus on early, preclinical-stage drug development. Anthropic also unveiled Claude Science, a new AI tool for research, and demonstrated early examples including a UCSF researcher using it to spot a viral contamination in minutes that his team had missed for an entire year. Pharma R&D timelines and success rates have long been constrained by information delays and operational bottlenecks. Novartis CEO Vas Narasimhan stated that AI tools could cut information and operational latency—which account for roughly 40 percent of total development time—potentially bringing drug development timelines down from twelve years to seven or eight years. Even modest improvements would matter: major pharma companies spend $150 to $200 billion（約32兆円） a year on R&D, and expanding the pool of treatable diseases could make previously unreachable drug targets viable.

   Claude Science analyzed 100 rare genetic diseases in under an hour and flagged 32 candidates for computational screening. Anthropic frames this drug discovery work as aligned with its nonprofit mission and as a way to build better AI models through firsthand experience in the sector. Other AI firms—including Deepmind (via Isomorphic Labs with Alphabet) and OpenAI—are also expanding into medicine and clinical tools.

2. 2

   Genesis AI model PEARL shows drug discovery can finally work—hitting real-world accuracy thresholds

   Genesis Molecular AI's PEARL model demonstrated on the OpenBind benchmark that it can accurately predict how small molecules bind to proteins, including modeling protein flexibility and induced-fit effects without fine-tuning on target-specific data. The model outperformed public competitors across evaluation metrics on 802 never-before-seen molecular complexes. Small-molecule drug discovery has long struggled because there are 10^60 drug-like molecules to search, and the properties that make a strong binder often conflict with those needed for the drug to reach its target in the body. PEARL's ability to model both ligand placement and protein adjustment together suggests that agentic drug-discovery loops—where AI iterates like a chemist, forming hypotheses and testing candidates—may now be practically feasible, potentially enabling 24/7 automated discovery cycles when paired with lab partners.

   The field has conventionally benchmarked poses at "2 Angstrom RMSD" accuracy, but Genesis argues that 1 Angstrom RMSD is the real threshold needed to correctly model molecular interactions like hydrogen bonds (which span only 0.6Å). PEARL's recent results suggest the community may be ready to abandon the weaker standard and pursue genuinely harder validation targets.

3. 3

   Meta's non-invasive brain-reading AI cuts word errors to 39%, closing gap with implants

   Meta researchers released Brain2Qwerty v2, which reconstructs typed sentences from brain signals measured outside the skull using magnetoencephalography (MEG). The system achieves a 39 percent word error rate, compared to 55 percent for previous methods, and requires ten times more training data than its predecessor to work without knowing exact keystroke timing. Invasive brain implants currently achieve below two percent word error rate, but they require surgery. This non-invasive approach—which works with portable room-temperature MEG sensors—offers a potential path toward clinical brain-to-text communication without surgery, though significant gaps remain and the system is not yet real-time capable.

   For the best participant, 28 percent of sentences decoded perfectly, and 47 percent contained at most one wrong word. The researchers found that collecting more recordings is a straightforward way to improve accuracy further, with no performance ceiling visible yet. Tests showed that even half the sensors deliver nearly full performance.

4. 4

   MindWalk Files Patent for AI Drug Discovery Data Layer

   MindWalk Holdings (NASDAQ: HYFT) announced the filing of European patent application EP26187897.9, which covers high-dimensional data structures for biological subsequences and property inference. The patent is intended to protect the enriched biological representation architecture that underpins MindWalk's HYFT® Technology, ReefIQ™ biological context layer, and LensAI™ reasoning workflows. As AI models become more widely available, the company believes lasting advantage in life sciences will come from the data layer—the structured, domain-specific biological representation that lets models and AI agents retrieve, compare, and reason over biology with traceable context. Spending on AI in drug discovery is projected to grow from approximately US$5 billion（約8000億円） in 2026 to more than US$8 billion（約1.3兆円） by 2030, sitting atop the more than US$250 billion（約40兆円） the pharmaceutical industry invests in research and development each year.

   The new filing builds on MindWalk's foundational HYFT patent (EP3881326A1) and protects a distinct computational layer—organizing biological meaning around characteristic patterns into a form that can be reused across MindWalk's infrastructure, customer programs, and AI workflows. Recent public work on scientific agents, including NVIDIA's BioNeMo Agent Toolkit and AstraZeneca's ChatInvent system, points to the need for domain-specific context, structured tool interfaces, and reliable input/output schemas when AI agents are deployed in real scientific workflows.

5. 5

   Eli Lilly backs AI drug startup Absci with $40M bet on injectable antibodies

   Eli Lilly led a $100 million（約160億円） stock offering in Absci, an AI drug company, investing $40 million（約64億円） itself. Absci designed ABS-201, an injectable antibody targeting hair loss and endometriosis, using generative AI. The deal closed the same day Absci released positive Phase 1 safety data. Lilly is betting that AI-designed drugs can succeed where traditional approaches haven't—no approved injectable antibody currently treats either condition ABS-201 targets. The deal signals confidence in Absci's strategy to use AI plus lower-cost clinical trials in China to compress development costs from $150 million（約240億円） to $15–$20 million（約32億円）, potentially reshaping drug economics.

   Absci's broader ambition is to eventually combine ABS-201 with a GLP-1 compound (used for weight loss) into a single shot. The injectable market is projected to be a $650 billion（約100兆円） opportunity in 2026, with GLP-1s alone projected to hit $190–$200 billion（約32兆円） by 2030. Whether ABS-201 becomes the first AI-designed antibody to prove efficacy in humans remains unknown.

6. 6

   Anthropic launches AI drug discovery program

   Anthropic launches AI drug discovery program