chemprop
Molecular Property Prediction with Message Passing Neural Networks
Decision gist · record as of 2026-08-14
Yes, if you work in computational chemistry, drug discovery, or molecular ML. Chemprop is actively maintained, well-cited in peer-reviewed research, and has a permissive license. The dependency stack is substantial but standard for this domain. Install friction is low. No known vulnerabilities. The v2 rewrite is recent; verify that your use case is covered in current documentation.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires PyTorch and RDKit; GPU support optional but recommended for large datasets.
- Python 3.11–3.14 only.
- Low install friction with a pure-Python wheel.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions, making it suitable for research, industry applications, and derivative work.
last release 2026-08-04 (10 days) · last repo commit 2026-08-04 · 2,435 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 110,881 downloads/mo, #12,438 on PyPI
Alternatives
Verify before relying
pip install chemprop
import chemprop
from chemprop import Chemprop
# Train a model on molecular SMILES and property data
model = Chemprop()
model.train(smiles_list, property_values)- Exact API surface and whether the snippet example reflects current v2.3.1 usage patterns
- Whether all 13 runtime dependencies are required for basic property prediction or only for advanced features
- Performance characteristics and typical training time for datasets of different sizes
What it is and what it does
Chemprop is a machine learning framework for predicting molecular properties using graph neural networks. It represents molecules as graphs and applies message passing to learn structural patterns that correlate with chemical properties, enabling applications in drug discovery, ADMET prediction, and molecular optimization.
The package underwent a major rewrite in v2.0.0 and now provides a modular, PyTorch-Lightning-based architecture. It depends on torch, lightning, numpy, pandas, rdkit, scikit-learn, and scipy, plus specialized chemistry libraries like descriptastorus and astartes. The framework is actively maintained, supports Python 3.11–3.14, and is permissively licensed under MIT.
Use it for
- Train models to predict absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties for drug candidates
- Discover novel antibiotics by predicting bioactivity against bacterial strains from molecular structures
- Build ensemble models for structure–activity relationship (SAR) analysis with interpretability
- Predict reaction properties or atom/bond-level predictions for chemical synthesis planning
- Benchmark molecular representations and compare graph neural network architectures on property datasets
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work in computational chemistry, drug discovery, or molecular ML.
Chemprop is actively maintained, well-cited in peer-reviewed research, and has a permissive license. The dependency stack is substantial but standard for this domain. Install friction is low. No known vulnerabilities. The v2 rewrite is recent; verify that your use case is covered in current documentation.
Install
chemprop on PyPI
Before you install
Low install friction with a pure-Python wheel. Active maintenance with a release 10 days ago and commits through 2026-08-04. Requires 13 runtime dependencies including torch, lightning, rdkit, and scikit-learn—a substantial but standard stack for molecular ML work.
Requires PyTorch and RDKit; GPU support optional but recommended for large datasets. Python 3.11–3.14 only.
License in practice
MIT license permits commercial and private use with minimal restrictions, making it suitable for research, industry applications, and derivative work.
Quickstart
pip install chemprop
import chemprop
from chemprop import Chemprop
# Train a model on molecular SMILES and property data
model = Chemprop()
model.train(smiles_list, property_values)Verify before relying
- Exact API surface and whether the snippet example reflects current v2.3.1 usage patterns
- Whether all 13 runtime dependencies are required for basic property prediction or only for advanced features
- Performance characteristics and typical training time for datasets of different sizes
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <3.15,>=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 13 packageslightningnumpypandasrdkitscikit-learnscipytorchastartesConfigArgParserichdescriptastoruscuik_molmaker_pinmyerson |
| Maintenance | Actively maintained 10 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 110,881 / month, #12,438 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: chemprop-2.3.1-py3-none-any.whl
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