ChemicalX
A PyTorch and TorchDrug based deep learning library for drug pair scoring, predicting interactions, side effects, and synergy.
What is ChemicalX?
ChemicalX is a deep learning library built on PyTorch and TorchDrug for drug pair scoring, which predicts outcomes like interactions, side effects, and synergy when two drugs are administered together. It provides data loaders, benchmark datasets, and state-of-the-art neural network architectures to address computational chemistry challenges. The library simplifies the implementation and evaluation of models for predicting drug pair effects in biological or chemical contexts.
Researchers and data scientists in computational chemistry, bioinformatics, and drug discovery who need to predict drug-drug interactions, polypharmacy side effects, or synergistic effects. It is also suitable for machine learning practitioners working on graph-based or molecular data tasks.
Developers choose ChemicalX because it offers a unified, high-level API with pre-implemented state-of-the-art models and integrated datasets, reducing the complexity of building and evaluating drug pair scoring systems. Its focus on reproducibility and ease of use, combined with support for both SMILES-based and graph neural network methods, makes it a comprehensive tool for advancing research in this niche.
Overview
A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)
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