GuacaMol
A Python package for benchmarking generative models in de novo molecular design.
What is GuacaMol?
GuacaMol is a Python package that provides benchmarks for evaluating generative models in de novo molecular design. It solves the problem of inconsistent evaluation in computational chemistry by offering standardized tests to measure how well models can generate novel, drug-like molecules. The package includes both distribution-learning and goal-directed benchmarks to assess different aspects of generative performance.
Computational chemists, machine learning researchers, and pharmaceutical scientists developing or using generative models for molecular design and drug discovery.
Researchers choose GuacaMol because it provides rigorous, reproducible benchmarks that enable fair comparison of generative models, includes standardized datasets, and offers containerized environments for consistent evaluation across different systems.
Overview
Benchmarks for generative chemistry
Use Cases
Best For
- Comparing different generative models for molecular design
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