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LynxKite provides GPU-accelerated graph analytics for applications including drug discovery.
About LynxKite
LynxKite is a GPU-accelerated graph analytics workspace for drug discovery. It combines graph reasoning, machine learning, and molecule analysis in one workflow for tasks such as target identification, compound generation and screening, toxicity prediction, and pharmacokinetic and pharmacodynamic modeling.
It works with NVIDIA BioNeMo, NVIDIA cuGraph/RAPIDS, RDKit, Hugging Face models, and custom LLMs and APIs, and includes 2D and 3D molecule visualization. An open-source core is available via pip install, while cluster deployment, additional enterprise features, and dedicated support require a commercial license.
Key features
- Graph reasoning with GNNs and GraphRAG over biomedical knowledge graphs
- GPU-accelerated compound generation and screening with BioNeMo and RDKit
- 2D and 3D molecule visualization in workflows
- Run Hugging Face models as scaling GPU services on Kubernetes
- LLM agents orchestrating open and proprietary models from genes to molecules
- Pharmacokinetic and pharmacodynamic modeling of absorption, distribution, metabolism, elimination
Best for: Teams in computational biology and drug discovery building graph-based target and compound workflows.
Good for
- Open-source core installable with pip install lynxkite
- Integrates with BioNeMo, cuGraph, RDKit, and custom LLMs and APIs
- GPU services start with workflows and scale to zero when idle
Watch out for
- Cluster deployment and dedicated support require a commercial license
LynxKite at a glance
- Website
- lynxkite.com
- Category
- AI & Machine Learning › AI Data & Analytics
- Type
- Website
- Pricing
- Not confirmed. Check the site
- Platforms
- Web
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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LynxKite FAQ
Is LynxKite open source?
LynxKite 2000:MM is open source and can be installed with pip install lynxkite. A commercial license adds cluster deployment, additional enterprise features, and dedicated support.
Which external models and tools can it use?
NVIDIA BioNeMo, NVIDIA cuGraph via RAPIDS, RDKit, Hugging Face models as Inference Microservices, and custom LLMs and APIs including OpenAI, Anthropic, or user-provided models.
How are Hugging Face models run in LynxKite?
As LynxKite Inference Microservices on Kubernetes using a @lim decorator on a Python row function, listed alongside NVIDIA NIMs and scaled to zero replicas when not needed.
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