deepmol
Contents:
Introduction
Google colabs to run AutoML
Available models
Installation
Input/output operations
Compound Standardization
Compound Featurization
Data Scaling
Feature Selection
Unsupervised Learning
Data Split
Unbalanced Datasets
Supervised Learning
Hyperparameter Optimization
Pipelines
Pipeline Optimization (AutoML)
Feature Importance
About Us
Related Publications
src
deepmol package
Subpackages
deepmol.base package
deepmol.compound_featurization package
deepmol.datasets package
deepmol.encoders package
deepmol.evaluator package
deepmol.feature_importance package
deepmol.feature_selection package
deepmol.imbalanced_learn package
deepmol.loaders package
deepmol.loggers package
deepmol.metrics package
deepmol.models package
deepmol.parallelism package
deepmol.parameter_optimization package
deepmol.pipeline package
deepmol.pipeline_optimization package
deepmol.scalers package
deepmol.splitters package
deepmol.standardizer package
deepmol.tokenizers package
deepmol.unsupervised package
deepmol.utils package
Module contents
deepmol
src
deepmol package
deepmol.compound_featurization package
deepmol.compound_featurization.neural_npfp package
View page source
deepmol.compound_featurization.neural_npfp package
Submodules
deepmol.compound_featurization.neural_npfp.model module
deepmol.compound_featurization.neural_npfp.utils module
Module contents