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534 articles, sorted by publication time.
AWS demonstrates moving an open-source protein binder design workflow to the cloud, making large-scale generation, folding evaluation, and screening of candidate molecules easier to scale; the real threshold remains experimental validation and the long road of drug development.
AI lab platforms are no longer just helping scientists write code and organize data; Anthropic’s new plan pushes the target toward drug candidates themselves, but a long road of experiments, toxicology, and clinical trials still lies between model output and usable therapies.
This is not another announcement packaging AI as a laboratory shortcut, but a sign that a large model company is beginning to step directly into early-stage drug R&D; the real test will unfold outside the computer, in experiments, toxicology, and clinical evidence.
A phase 1 study using non-genetically modified autologous T cells to target three tumor antigens at once has produced preliminary safety and efficacy signals in some high-risk pediatric central nervous system tumors, but a key step remains before it can be shown to extend survival.
A market forecast portrays AI-designed surgical implants as a fast-growing medical technology field; the real issue is not how large the numbers are, but whether customized designs can enter routine surgery with verifiable safety.
This collaboration, worth up to $600 million, is not just another licensing deal for an AI drug development company; it pushes the question back into the clinic and the regulatory arena: can drug candidates proposed by models prove in human trials that they are not only faster, but more reliable?
As drug R&D increasingly relies on algorithms, what is truly scarce may not only be models, but the data foundation that allows complex biological signals to be reliably organized, queried, and validated.
If this deal advances under the announced terms, it will once again show that large pharmaceutical companies are willing to bet on AI drug discovery; but what can truly change R&D efficiency is not the speed at which models generate molecules, but whether drug candidates can pass through biological validation, clinical trials, and regulatory review.
The debut of Claude Science is not just the release of another AI tool, but an attempt by a technology company to connect models, data analysis, and candidate drug discovery into a single R&D chain; the real dividing line will appear when computer predictions move into wet-lab experiments and clinical evidence.
Membrane proteins have long been difficult targets for drug development and structural analysis; if a new wave of AI-designed proteins can reliably lock their shapes in place, more receptors and channels may move from being "hard to see" to being researchable and designable.
More than 50 pharmaceutical companies are investing in research and development, showing that HER2-positive breast cancer remains a core battleground in oncology drug development. But pipeline momentum does not equal clinical victory; the real key is whether therapies can extend survival, reduce toxicity, and answer the difficult question of treatment sequencing.
Antiviral drug development must not only prove that a drug can suppress a virus, but also explain how the virus may escape. The FDA’s new guidance spells out more specific requirements for submitting next-generation sequencing data, turning genetic data from a research appendix into evidence that reviewers can rerun and verify.