Inductive Bio Connects ADMET Prediction Models to Claude for Drug Discovery

Jul 1, 2026
Inductive Bio has joined Anthropic's life sciences ecosystem and launched a connector that integrates its ADMET prediction models into Claude, allowing scientists to analyze drug properties directly within the AI assistant.

Inductive Bio announced in a press release that it has joined Anthropic's life sciences ecosystem and introduced a Model Context Protocol connector for Claude by Anthropic. The integration makes Inductive Bio's ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) prediction models available directly within Claude, giving drug discovery scientists access to predictive insights in their existing workflows.

The connector allows users of Claude and Claude Science to request on demand predictions for ADMET properties of chemical structures while reasoning about results through natural language. This feature enables scientists to evaluate how compounds behave in the body alongside other design considerations.

Inductive Bio's ADMET models have been independently validated, ranking first among more than 370 entries in the OpenADMET-ExpansionRx blind challenge. The company stated that chemical structures submitted through Claude are not stored or used to train its models. Researchers can also access Inductive Bio's broader suite of ADMET and pharmacokinetic models or request versions fine tuned to their own chemical datasets.

We hope you enjoyed this article

Consider subscribing to one of our newsletters like Life AI Weekly or Daily AI Brief.

Also, consider following us on social media:

Free newsletter

Life AI Weekly

Weekly coverage of AI applications in healthcare, drug development, biotechnology research, and genomics breakthroughs.

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more
Free, six days a week

Daily AI Brief: the AI news that matters, in your inbox.