Cheminformatics Microservice

🌐 Overview

The Cheminformatics Microservice is a set of essential and valuable services designed to support cheminformatics through API calls. It primarily works with SMILES-based inputs and offers functionalities such as:

• Translating between different machine-readable representations
• Calculating Natural Product (NP) likeliness scores
• Visualizing chemical structures
• Generating descriptors

Additionally, it hosts instances of:

• STOUT: A deep learning model for IUPAC name generation
• DECIMER: A deep learning model for optical chemical structure recognition

📚 Documentation

Comprehensive documentation is available at: https://docs.api.naturalproducts.net/

API Swagger Docs

• Production: https://api.naturalproducts.net/latest/docs
• Development: https://dev.api.naturalproducts.net/latest/docs

💻 Installation

You can run Cheminformatics Microservice in multiple ways:

1. As a standalone application using a Python virtual environment
2. Via Docker
3. Deploy to a Kubernetes cluster using Helm charts

For detailed instructions, please refer to:

• Docker Installation Guide
• Kubernetes Cluster Deployment Guide
• Standalone Installation Guide

📈 Load Ramping Test Results

View the latest load ramping test results from September 29, 2023: Test Results Discussion

📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

📰 Citation

Paper

Chandrasekhar, V., Sharma, N., Schaub, J. et al. Cheminformatics Microservice: unifying access to open cheminformatics toolkits. J Cheminform 15, 98 (2023). https://doi.org/10.1186/s13321-023-00762-4

Software

Venkata, C., Sharma, N., & Rajan, K. (2023). Cheminformatics Microservice (Version v2.6.0) [Computer software]. https://zenodo.org/records/13867839

🔧 Maintenance

Cheminformatics Microservice and Natural Products Online are developed and maintained by the Steinbeck group at the Friedrich Schiller University Jena, Germany.

The code for this web application is released under the MIT license. Copyright © CC-BY-SA 2023

💡 Acknowledgments

Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the National Research Data Infrastructure – NFDI4Chem – Project number: 441958208 and ChemBioSys (Project INF) - Project number: 239748522 - SFB 1127.