# Anthropic launches AI platform for scientists and plans to develop drugs

> Anthropic launched Claude Science for scientific computing and plans to develop its own drugs. Separately, Phase III trials began for the first AI-designed drug.

- Type: AI news
- Page: https://completeaitraining.com/news/anthropic-launches-ai-platform-for-scientists-and-plans-to/
- Published: 2026-07-10
- Updated: 2026-07-10
- Author: Jeroen Erne
- Jobs: IT and development
- Reading time: 1 min

Anthropic has launched Claude Science, an AI platform that the company has been testing with researchers for months. The platform is designed for scientific computing tasks such as single-cell RNA sequencing analysis, CRISPR screening design, protein structure prediction, and chemoinformatics. The company also plans to develop its own drugs, focusing on diseases that receive little research funding.

#### A platform for research automation

During the beta period, researchers used Claude Science across a range of computational biology tasks. The platform, built on Anthropic's [Claude](https://completeaitraining.com/tag/claude/) models, supports tasks that require handling large datasets and complex models. This includes everything from genomics to drug candidate screening.

#### Anthropic's drug development ambitions

Separately, Anthropic aims to develop drugs internally. The company is targeting diseases with low research investment, a move that could accelerate treatments for neglected conditions. This coincides with a broader push in the industry: Phase III clinical trials have begun for INS018\_055 (rentosertib), the first drug entirely designed with help from AI, which treats idiopathic pulmonary fibrosis. Industry representatives said drugs fully developed using AI could reach the market by the end of the decade.

#### Why this matters for IT and Development

For developers and IT professionals, platforms like Claude Science represent a new class of AI-powered scientific tools. Building and deploying these systems requires expertise in model orchestration, data pipelines, and infrastructure scaling. The [AI for Science & Research](https://completeaitraining.com/tag/science-and-research/) space is growing, and with it, demand for engineers who can bridge the gap between AI models and domain-specific scientific workflows. As drug companies and research labs adopt AI, the need for developers who understand both machine learning and the operational demands of life sciences will only increase.

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