Life Sciences AI

Drug programs can be steered using continually updated evidence and probability-of-success estimates, helping identify patient subgroups and change course during trials

Pathos AI develops oncology drugs using Foundry, an agentic platform that continuously analyzes clinical evidence to select assets and guide development decisions.

AI-led scientific workflows can connect patient evidence to testable hypotheses, with biomedical experts and wet labs validating promising results

Owkin develops K Pro, an autonomous AI scientist that combines multimodal patient data, specialized agents, and laboratory validation to investigate biomedical questions.

The physical execution layer for autonomous science

Opentrons connects AI models to open laboratory robots, turning experiment design, execution, and structured results into a continuous learning loop for autonomous science.

Drug development can advance faster when clinical assets, operational workflows, and accumulated evidence are coordinated through software rather than fragmented manual processes

Formation Bio acquires clinical-stage medicines and develops them using an AI-native platform, combining drug selection, trial operations, and reusable data infrastructure.

Proxima Bio, formerly VantAI, is an AI-native platform for proximity-based drug discovery, mapping protein-protein interactions to design medicines that reshape protein networks rather than inhibit single targets

Scientific AI can become more useful by learning the mechanisms behind complex systems and using those explanations to guide new investigations

Output Labs develops AI systems for mechanistic reasoning, aiming to model how complex systems work and apply those explanations to scientific problems.

Find therapeutic targets hidden in RNA splicing

Envisagenics analyzes RNA splicing with AI to identify disease-specific drug targets, advancing therapeutic discovery for cancer and neurodegenerative conditions through its SpliceCore platform.