Showing 3 open source projects for "python molecular"

View related business solutions
  • Our Free Plans just got better! | Auth0 Icon
    Our Free Plans just got better! | Auth0

    With up to 25k MAUs and unlimited Okta connections, our Free Plan lets you focus on what you do best—building great apps.

    You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
    Try free now
  • Context for your AI agents Icon
    Context for your AI agents

    Crawl websites, sync to vector databases, and power RAG applications. Pre-built integrations for LLM pipelines and AI assistants.

    Build data pipelines that feed your AI models and agents without managing infrastructure. Crawl any website, transform content, and push directly to your preferred vector store. Use 10,000+ tools for RAG applications, AI assistants, and real-time knowledge bases. Monitor site changes, trigger workflows on new data, and keep your AIs fed with fresh, structured information. Cloud-native, API-first, and free to start until you need to scale.
    Try for free
  • 1
    Chemprop

    Chemprop

    Message Passing Neural Networks for Molecule Property Prediction

    Chemprop is a repository containing message-passing neural networks for molecular property prediction.
    Downloads: 2 This Week
    Last Update:
    See Project
  • 2
    DeepChem

    DeepChem

    Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, etc

    ...All tutorials are designed to be run on Google collab (or locally if you prefer). Tutorials are arranged in a suggested learning sequence that will take you from beginner to proficient at molecular machine learning and computational biology more broadly.
    Downloads: 1 This Week
    Last Update:
    See Project
  • 3
    Spektral

    Spektral

    Graph Neural Networks with Keras and Tensorflow 2

    Spektral is a Python library for graph deep learning, based on the Keras API and TensorFlow 2. The main goal of this project is to provide a simple but flexible framework for creating graph neural networks (GNNs). You can use Spektral for classifying the users of a social network, predicting molecular properties, generating new graphs with GANs, clustering nodes, predicting links, and any other task where data is described by graphs.
    Downloads: 0 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next