Welcome to QSARtuna Documentation!
Welcome to QSARtuna: QSAR using Optimization for Hyperparameter Tuning.
Build QSAR models with hyperparameters optimized by Optuna.
Documentation:
- Intro and Quick Start
- Jupyter Notebook: Preprocessing Data for QSARtuna
- QSARtuna CLI Tutorial
- This tutorial
- Background
- Preparation
- Preprocessing: splitting data into train and test sets, and removing duplicates
- Choosing scoring function
- Advanced functoinaility: algorithms & runs
- Probabilistic Random Forest (PRF)
- ChemProp
- Probability calibration (classification)
- Uncertainty estimation
- Explainability
- Log transformation
- Covariate modelling
- Advanced options for QSARtuna runs
- Preexisting models: Convert scikit-learn(-like) models to QSARtuna models
- Preexisting models: Using prexisting models as custom models for Optimisation or Build
- AutoML (Automated model retraining)
- List of available ML algorithms
- AdaBoostClassifier
- Lasso
- KNeighborsClassifier
- KNeighborsRegressor
- LogisticRegression
- PLSRegression
- RandomForestClassifier
- RandomForestRegressor
- Ridge
- SVC
- SVR
- XGBRegressor
- PRFClassifier
- ChemPropRegressor
- ChemPropClassifier
- ChemPropHyperoptClassifier
- ChemPropHyperoptRegressor
- ChemPropHyperoptRegressorPretrained
- CalibratedClassifierCVWithVA
- Mapie
- CustomRegressionModel
- CustomClassificationModel
- List of available molecular descriptors
- Avalon
- ECFP
- ECFP_counts
- PathFP
- MACCS_keys
- UnscaledPhyschemDescriptors
- UnscaledJazzyDescriptors
- UnscaledZScalesDescriptors
- PhyschemDescriptors
- JazzyDescriptors
- PrecomputedDescriptorFromFile
- ZScales
- SmilesFromFile
- SmilesAndSideInfoFromFile
- ScaledDescriptor
- CompositeDescriptor
- AmorProtDescriptors
- UnscaledMAPC
- UnscaledZScalesDescriptors
- MAPC
- ZScalesDescriptors
- List of available evaluation splits
- List of available data transform
- List of available deduplicators