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Chainer Chemistry

Chainer Chemistry is a Python deep-learning library based on Chainer, designed for biology and chemistry research. It supports graph neural networks for chemical-property prediction and includes examples for training, inference and model evaluation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Chainer Chemistry — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when a Python research workflow needs Chainer-based deep-learning models and examples for molecular or graph data, and the project’s documented dependency versions fit the environment.

Who it is for

  • Researchers applying deep learning to chemical or biological data.
  • Python developers building or evaluating graph-neural-network experiments.

Documented uses

  • Train a model on a dataset.
  • Run inference on a dataset with a pretrained model.
  • Evaluate and report model performance.
  • Explore listed graph-neural-network architectures for chemical-property prediction.
Before you choose

Requirements & limits

The repository identifies the project as open source under the MIT license.

  • RDKit 2019.03.2.0 is a manual prerequisite; the README says pip adds the other listed dependencies.
  • The documented Python and Chainer compatibility depends on the Chainer Chemistry release.
  • The README says development on the master branch is limited to serious bug fixes and maintenance.
  • The model list includes graph architectures such as SchNet, GAT, GIN, MEGNet and CGCNN.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install Chainer Chemistry and its RDKit prerequisite

Set up the library using the documented pip installation and install the separately required RDKit version.

Before you start

  • Python environment; the README lists Python 3.6 and 3.7 for versions 0.7.0–0.7.1 and the master branch.
  • RDKit 2019.03.2.0, installed separately.
  • Conda for the README's example RDKit installation; it specifies conda 4.6.14 in the base environment.

Steps

  1. Install the library with the command below.

    Documented command
    pip install chainer-chemistry

  2. Install RDKit 2019.03.2.0 separately; the README gives conda install -n base conda==4.6.14 followed by conda install -c rdkit rdkit==2019.03.2.0 as an example.

Expected outcome

The documented installation steps provide the Chainer Chemistry package and its separately installed RDKit dependency.

Guide limitations

  • The README's dependency compatibility table is release-specific; check it before choosing a Chainer Chemistry version.
  • The README notes that the master branch is limited to serious bug fixes and maintenance.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Chainer ChemistryTrain a model on a dataset.Linux, macOS, WindowsThe repository identifies the project as open source under the MIT license.
Keras2CppExport a Keras model’s architecture and weights to a plain-text file.LinuxThe public GitHub repository specifies an MIT license.
Omega AIConstruct neural networks and train or test models in Java.ServerThe project is identified as open source and uses the Apache-2.0 license.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
7 November 2026
Original website title
chainer/chainer-chemistry · Official homepage title · retained in its source language
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