MoleculeNet: a benchmark for molecular machine learning† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc02664a
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چکیده
1 Model Training and Hyperparameter Optimization All models were trained on Stanford’s GPU clusters via DeepChem. No model was allowed to train for more than 10 hours(time profile in Table S1. Users can reproduce benchmarks locally by following directions from DeepChem. Hyperparameters were determined using Gaussian Process Optimization via pyGPGO(https://github.com/hawk31/pyGPGO), with max number of iterations set to 20. Optimized hyperparameters for each model are listed, detailed hyperparameters can be found on Deepchem. 1.1 Logistic Regression (Logreg) • Learning rate • L2 regularization • Batch size
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MoleculeNet: A Benchmark for Molecular Machine Learning
Molecular machine learning has been maturing rapidly over the last few years. Improved methods and the presence of larger datasets have enabled machine learning algorithms to make increasingly accurate predictions about molecular properties. However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are...
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