How to install Indigo for Python quickly?

You can install it via pip with 'pip install epam.indigo' from PyPI, but for advanced features or custom builds, you'll need to compile from source using the CMake scripts detailed in the README, which involves additional dependencies.

Indigo vs RDKit: which is better for my project?

Indigo excels in database integration and multi-language support, making it ideal for enterprise applications with SQL or Elasticsearch backends. RDKit is more Python-centric with a larger community for algorithmic research, so choose based on your stack and search needs.

Can Indigo handle SMILES and SDF files?

Yes, Indigo supports common chemical formats like SMILES and SDF through its core library, with utilities like indigo-cano for canonical SMILES and ChemDiff for visual comparison, as mentioned in the feature list.

How to use Indigo with Elasticsearch?

Use Bingo-Elastic, which provides Java and Python APIs for chemistry search in Elasticsearch. You'll need to build and integrate these APIs, following the separate READMEs in the repository for setup and configuration.

What databases does Bingo support for chemical search?

Bingo supports Oracle, Microsoft SQL Server, and PostgreSQL databases, allowing you to perform chemistry searches directly via SQL extensions, which is a key feature for relational database workflows in cheminformatics.

Is Indigo good for web-based molecular editors?

Yes, Indigo's WebAssembly bindings enable cheminformatics computations in browsers, making it suitable for web apps. However, building the WASM version requires Emscripten SDK and Ninja, adding complexity to the development setup.

Open-Awesome

Indigo

Apache-2.0C++indigo-1.43.0

A universal cheminformatics toolkit with database search engines, a core library, and utilities for molecular processing.

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What is Indigo?

Indigo is an open-source cheminformatics toolkit that provides a universal library, database search engines, and utilities for processing chemical data. It solves the problem of fragmented cheminformatics tools by offering a cohesive suite for molecular modeling, search, and visualization across multiple programming languages and databases.

Target Audience

Cheminformatics researchers, computational chemists, and software developers working in pharmaceutical, biotech, or chemical industries who need to integrate chemical data processing into their applications.

Value Proposition

Developers choose Indigo for its comprehensive feature set, cross-platform bindings, and high-performance search capabilities, which eliminate the need to cobble together multiple specialized libraries.

Overview

Universal cheminformatics toolkit, utilities and database search tools

Use Cases

Best For

Adding chemical structure search to relational databases like PostgreSQL or SQL Server
Building web-based molecular editors or visualization tools using WebAssembly
Developing cheminformatics pipelines in Python, Java, or .NET applications
Creating custom chemical database solutions with Elasticsearch integration
Performing combinatorial chemistry experiments and reaction analysis
Comparing and visualizing differences between chemical datasets

Not Ideal For

Projects requiring only basic molecular visualization without database search capabilities
Teams lacking expertise in C++ build systems or unwilling to manage complex dependencies
Startups or small projects needing quick, out-of-the-box cheminformatics with minimal setup

Pros & Cons

Pros

Multi-Platform Bindings

Indigo offers native bindings for .NET, Java, Python, R, and WebAssembly, enabling seamless integration into diverse tech stacks without rewriting core logic, as evidenced by the availability on NuGet, Maven, and PyPI.

Database Search Integration

Includes Bingo for relational databases (Oracle, SQL Server, PostgreSQL) and Bingo-Elastic for Elasticsearch, providing efficient chemistry search directly in databases, which is a standout feature for data-heavy applications.

Comprehensive Utility Suite

Comes with tools like Legio for combinatorial chemistry, ChemDiff for visual file comparison, and rendering utilities, covering a wide range of cheminformatics tasks beyond core library functions.

Cons

Complex Build Process

Building from source requires CMake 3.4+, multiple compilers, and numerous dependencies (e.g., JDK, .NET, Emscripten for WASM), making setup cumbersome and error-prone for non-expert users.

Limited Beginner Documentation

The README focuses on build instructions rather than usage examples, and external documentation may not adequately guide new developers through common tasks, increasing the learning curve.

Heavy Dependency Management

Running tests or full builds necessitates installing many Python packages (e.g., waitress, flasgger, psycopg2) and system tools, which can lead to environment conflicts and maintenance overhead.

Frequently Asked Questions

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