{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"Chemprop trains and deploys message passing neural networks to predict molecular properties from chemical structures, using graph neural networks to learn from molecular representations.","skillfed_tags":["molecular-ml","drug-discovery","graph-neural-networks"],"use_cases":["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\u2013activity 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"],"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.\n\nThe 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\u20133.14, and is permissively licensed under MIT.","worth_installing":"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."},"id":"chemprop","links":{"html":"https://skillfed.io/packages/chemprop","md":"https://skillfed.io/packages/chemprop.md","pypi":"https://pypi.org/project/chemprop/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-04","license_spdx":null,"license_treatment":"permissive","name":"chemprop","python_support":"supports_current","summary":"Molecular Property Prediction with Message Passing Neural Networks"},"popularity":{"monthly_downloads":110881,"position":12438,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.3.1"}