v2.0.0

Change Log

From v1.3.1 to v2.0.0

Fixes

• more robust error handling of invalid molecules in MoleculeTable
• Not all scorers in supported_scoring were actually working in the multi-class case, the scorer support is now
  divided by single and multiclass support (moved to metrics.py, see also New Features).
• Instead of all smiles, only invalid smiles are now printed to the log when they are removed.
• problems with PaDEL descriptors and fingerprints on Linux were fixed
• fixed serialization issues with DataFrameDescriptorSet and saving and loading of MSA for PCM descriptor calculations
• the Papyrus adapter was fixed so that the quality and data set filtering options work properly (before only high quality Papyrus++ data was fetched no matter the options)
• previously, in some cases cross-validation splits might not have been shuffled during hyperparameter optimization and evaluation on cross-validation folds (this might have resulted in suboptimal cross-validation performance and bad choices of hyperparameters), a fix was made in b029e78
• score_func can now be set in QSPRModel.

Changes

• Hyperparameter optimization moved to a separate class from QSPRModel.bayesOptimization and QSPRModel.gridSearch to OptunaOptimization and GridSearchOptimization in the new module qsprpred.models.param_optimzation with a base clase HyperParameterOptimization in qsprpred.models.interfaces.
• ⚠️ Important! ⚠️ QSPRModel attribute model now called estimator, which is always an instance of alg, while alg may no longer be an instance but only a Type.
• Converting input data for qsprpred.models.neural_network.Base to dataloaders now executed in the fit and predict functions instead of in the qspred.deep.models.QSPRDNN class.
• MoleculeTable now uses a custom index. When a MoleculeTable is created a new column (QSPRID) is added (overwritten if already present), which is then used as the index of the underlying data frame.
  • It is possible to override this with a custom index by passing index_cols to the MoleculeTable constructor. These columns will be then used as index or a multi-index if more than one column is passed.
  • Due to this change, scaffoldsplit now uses these IDs instead of unreliable SMILES strings (see documentation for the new API).
• If there are invalid molecules in MoleculeTable, addDescriptors now fails by default. You can disable this by passing fail_on_invalid=False to the method.
• To support multitask modelling, the representation of the target in the QSPRdataset has changed to a list of
  TargetPropertys (see New Features). These can be automatically initizalid from dictionaries in the QSPRdataset
  init.
• A fill_value argument was also added to the predict_CLI script to allow for filling missing values in the
  prediction data set as well.
• ⚠️ Important! ⚠️ setup.py and setup.cfg were substituted with pyproject.toml and MANIFEST.in. A lighter version of the package is now the default installation option!!!
  • Installation options for the optional dependencies are described in README.md
  • CI scripts were modified to test the package on the full version. See changes in .gitlab-ci.yml.
  • Features using the extra dependencies were moved to qsprpred.extra and qsprpred.deep subpackages. The structure of the subpackages is the same as of the main package, so you just need to remember to use qsprpred.extra or qsprpred.deep instead of just qsprpred in your imports if you were using these features from the main package before.
• The way descriptors are stored in MoleculeTable was changed. They now reside in their own DescriptorTable instances that are linked to the orginal MoleculeTable
  • This change was made to allow several types of descriptors to be calculated and used efficiently (facilitated by a the DescriptorsCalculators interface)
  • Unfortunately, this change is not backwards compatible, so previously pickled MoleculeTable instances will not work with this version. There were also changes to how models handle multiple descriptor types, which also makes them incompatible with previous versions. However, this can be fixed by modifying the old JSON files as illustrated in commits 7d3f863 and 6564f02.
• 'LowVarianceFilter` now includes boundary in the filtered features, e.g. if threshold is 0.1, also features that
  have a variance of 0.1 will be removed.
• Added the ExtendedValenceSignature molecular descriptor based on Jean-Loup Faulon's work.
• removed default parameter setting scikit-learn SVC and SVR max_iter 10000.
• added matthews_corrcoef to the supported metrics for binary classification.

New Features

• New feature split ManualSplit for splitting data by a user-defined column
• The index of the MoleculeTable can now be used to relate cross-validation and test outputs to the original molecules. Therefore, the index is now also saved in the model training outputs.
• the Papyrus.getData() method now accepts activity_types parameter to select a list of activity types to get.
• Added the checkMols method to MoleculeTable to use for indication of invalid molecules in the data.
• Support for Sklearn Multitask modelling
• New class abstract class Metric, which is an abstract base class that allows for creating custom scorers.
• Subclass SklearnMetric of the Metric class, this class wraps the sklearn metrics, to allow for checking
  the compatibility of each Sklearn scoring function with the QSPRSklearn model type.
• New class TargetProperty, to allow for multitask modelling, a QSPRdataset has to have the option of multiple
  targetproperties. To support this a targer property is now defined seperatly from the dataset as a TargetProperty
  instance, which holds the information on name, TargetTask (see also Changes) and threshold of the property.
• Support for protein descriptors and PCM modeling was added.
  • The PCMDataSet class was introduced that extends QSPRDataset and adds the addProteinDescriptors method, which can be used to calculate protein descriptors by linking information from the table with sequencing data.
• Support for precalculated descriptors was added with addCustomDescriptors method of MoleculeTable.
  • It allows for adding precalculated descriptors to the MoleculeTable by linking the information from the table with external precalculated descriptors.
• The tutorial was improved with more detailed sections on data preparation and PCM modelling added.
• We agreed on and adopted a style guide for contributions to the package. This is described and exemplified in docs/style_guide.py. This is also supported by several development tools that were configured to check and automatically format the code. Instructions are included in the example file as well.
• Style guide implemented. As a consequence, some classes, methods, and attributes were renamed to comply with the style guide. Some changes were:
  • Fingerprint functions from RDKit are now also implemented. For checking the available fingerprints in qsprpred, the user can now access AVAIL_FPS through from qsprpred.data.utils.descriptor_utils.fingerprints import AVAIL_FPS.
  • Fingerprint abstract base class now moved to qsprpred.data.utils.descriptor_utils.interfaces.
  • Instance attributes are now written in camelCase, and method arguments are snake_case. As an example of this, the old parameter descsets from MoleculeDescriptorsCalculator is now renamed as desc_sets, stored as the attribute self.descSets. Functions are still written in snake_case.