Deepchem

# DeepChem API Reference

This document provides a comprehensive reference for DeepChem's core APIs, organized by functionality.

## Data Handling

### Data Loaders

#### File Format Loaders
- **CSVLoader**: Load tabular data from CSV files with customizable feature handling
- **UserCSVLoader**: User-defined CSV loading with flexible column specifications
- **SDFLoader**: Process molecular structure files (SDF format)
- **JsonLoader**: Import JSON-structured datasets
- **ImageLoader**: Load image data for computer vision tasks

#### Biological Data Loaders
- **FASTALoader**: Handle protein/DNA sequences in FASTA format
- **FASTQLoader**: Process FASTQ sequencing data with quality scores
- **SAMLoader/BAMLoader/CRAMLoader**: Support sequence alignment formats

#### Specialized Loaders
- **DFTYamlLoader**: Process density functional theory computational data
- **InMemoryLoader**: Load data directly from Python objects

### Dataset Classes

- **NumpyDataset**: Wrap NumPy arrays for in-memory data manipulation
- **DiskDataset**: Manage larger datasets stored on disk, reducing memory overhead
- **ImageDataset**: Specialized container for image-based ML tasks

### Data Splitters

#### General Splitters
- **RandomSplitter**: Random dataset partitioning
- **IndexSplitter**: Split by specified indices
- **SpecifiedSplitter**: Use pre-defined splits
- **RandomStratifiedSplitter**: Stratified random splitting
- **SingletaskStratifiedSplitter**: Stratified splitting for single tasks
- **TaskSplitter**: Split for multitask scenarios

#### Molecule-Specific Splitters
- **ScaffoldSplitter**: Divide molecules by structural scaffolds (prevents data leakage)
- **ButinaSplitter**: Clustering-based molecular splitting
- **FingerprintSplitter**: Split based on molecular fingerprint similarity
- **MaxMinSplitter**: Maximize diversity between training/test sets
- **MolecularWeightSplitter**: Split by molecular weight properties

**Best Practice**: For drug discovery tasks, use ScaffoldSplitter to prevent overfitting on similar molecular structures.

### Transformers

#### Normalization
- **NormalizationTransformer**: Standard normalization (mean=0, std=1)
- **MinMaxTransformer**: Scale features to [0,1] range
- **LogTransformer**: Apply log transformation
- **PowerTransformer**: Box-Cox and Yeo-Johnson transformations
- **CDFTransformer**: Cumulative distribution function normalization

#### Task-Specific
- **BalancingTransformer**: Address class imbalance
- **FeaturizationTransformer**: Apply dynamic feature engineering
- **CoulombFitTransformer**: Quantum chemistry specific
- **DAGTransformer**: Directed acyclic graph transformations
- **RxnSplitTransformer**: Chemical reaction preprocessing

## Molecular Featurizers

### Graph-Based Featurizers
Use these with graph neural networks (GCNs, MPNNs, etc.):

- **ConvMolFeaturizer**: Graph representations for graph convolutional networks
- **WeaveFeaturizer**: "Weave" graph embeddings
- **MolGraphConvFeaturizer**: Graph convolution-ready representations
- **EquivariantGraphFeaturizer**: Maintains geometric invariance
- **DMPNNFeaturizer**: Directed message-passing neural network inputs
- **GroverFeaturizer**: Pre-trained molecular embeddings

### Fingerprint-Based Featurizers
Use these with traditional ML (Random Forest, SVM, XGBoost):

- **MACCSKeysFingerprint**: 167-bit structural keys
- **CircularFingerprint**: Extended connectivity fingerprints (Morgan fingerprints)
  - Parameters: `radius` (default 2), `size` (default 2048), `useChirality` (default False)
- **PubChemFingerprint**: 881-bit structural descriptors
- **Mol2VecFingerprint**: Learned molecular vector representations

### Descriptor Featurizers
Calculate molecular properties directly:

- **RDKitDescriptors**: ~200 molecular descriptors (MW, LogP, H-donors, H-acceptors, TPSA, etc.)
- **MordredDescriptors**: Comprehensive structural and physicochemical descriptors
- **CoulombMatrix**: Interatomic distance matrices for 3D structures

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