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Zhongwei Li
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skills/molfeat/references/available_featurizers.md
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skills/molfeat/references/available_featurizers.md
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# Available Featurizers in Molfeat
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This document provides a comprehensive catalog of all featurizers available in molfeat, organized by category.
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## Transformer-Based Language Models
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Pre-trained transformer models for molecular embeddings using SMILES/SELFIES representations.
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### RoBERTa-style Models
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- **Roberta-Zinc480M-102M** - RoBERTa masked language model trained on ~480M SMILES strings from ZINC database
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- **ChemBERTa-77M-MLM** - Masked language model based on RoBERTa trained on 77M PubChem compounds
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- **ChemBERTa-77M-MTR** - Multitask regression version trained on PubChem compounds
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### GPT-style Autoregressive Models
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- **GPT2-Zinc480M-87M** - GPT-2 autoregressive language model trained on ~480M SMILES from ZINC
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- **ChemGPT-1.2B** - Large transformer (1.2B parameters) pretrained on PubChem10M
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- **ChemGPT-19M** - Medium transformer (19M parameters) pretrained on PubChem10M
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- **ChemGPT-4.7M** - Small transformer (4.7M parameters) pretrained on PubChem10M
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### Specialized Transformer Models
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- **MolT5** - Self-supervised framework for molecule captioning and text-based generation
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## Graph Neural Networks (GNNs)
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Pre-trained graph neural network models operating on molecular graph structures.
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### GIN (Graph Isomorphism Network) Variants
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All pre-trained on ChEMBL molecules with different objectives:
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- **gin-supervised-masking** - Supervised with node masking objective
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- **gin-supervised-infomax** - Supervised with graph-level mutual information maximization
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- **gin-supervised-edgepred** - Supervised with edge prediction objective
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- **gin-supervised-contextpred** - Supervised with context prediction objective
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### Other Graph-Based Models
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- **JTVAE_zinc_no_kl** - Junction-tree VAE for molecule generation (trained on ZINC)
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- **Graphormer-pcqm4mv2** - Graph transformer pretrained on PCQM4Mv2 quantum chemistry dataset for HOMO-LUMO gap prediction
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## Molecular Descriptors
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Calculators for physico-chemical properties and molecular characteristics.
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### 2D Descriptors
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- **desc2D** / **rdkit2D** - 200+ RDKit 2D molecular descriptors including:
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- Molecular weight, logP, TPSA
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- H-bond donors/acceptors
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- Rotatable bonds
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- Ring counts and aromaticity
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- Molecular complexity metrics
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### 3D Descriptors
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- **desc3D** / **rdkit3D** - RDKit 3D molecular descriptors (requires conformer generation)
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- Inertial moments
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- PMI (Principal Moments of Inertia) ratios
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- Asphericity, eccentricity
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- Radius of gyration
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### Comprehensive Descriptor Sets
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- **mordred** - Over 1800 molecular descriptors covering:
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- Constitutional descriptors
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- Topological indices
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- Connectivity indices
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- Information content
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- 2D/3D autocorrelations
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- WHIM descriptors
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- GETAWAY descriptors
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- And many more
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### Electrotopological Descriptors
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- **estate** - Electrotopological state (E-State) indices encoding:
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- Atomic environment information
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- Electronic and topological properties
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- Heteroatom contributions
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## Molecular Fingerprints
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Binary or count-based fixed-length vectors representing molecular substructures.
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### Circular Fingerprints (ECFP-style)
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- **ecfp** / **ecfp:2** / **ecfp:4** / **ecfp:6** - Extended-connectivity fingerprints
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- Radius variants (2, 4, 6 correspond to diameter)
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- Default: radius=3, 2048 bits
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- Most popular for similarity searching
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- **ecfp-count** - Count version of ECFP (non-binary)
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- **fcfp** / **fcfp-count** - Functional-class circular fingerprints
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- Similar to ECFP but uses functional groups
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- Better for pharmacophore-based similarity
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### Path-Based Fingerprints
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- **rdkit** - RDKit topological fingerprints based on linear paths
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- **pattern** - Pattern fingerprints (similar to MACCS but automated)
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- **layered** - Layered fingerprints with multiple substructure layers
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### Key-Based Fingerprints
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- **maccs** - MACCS keys (166-bit structural keys)
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- Fixed set of predefined substructures
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- Good for scaffold hopping
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- Fast computation
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- **avalon** - Avalon fingerprints
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- Similar to MACCS but more features
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- Optimized for similarity searching
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### Atom-Pair Fingerprints
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- **atompair** - Atom pair fingerprints
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- Encodes pairs of atoms and distance between them
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- Good for 3D similarity
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- **atompair-count** - Count version of atom pairs
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### Topological Torsion Fingerprints
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- **topological** - Topological torsion fingerprints
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- Encodes sequences of 4 connected atoms
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- Captures local topology
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- **topological-count** - Count version of topological torsions
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### MinHashed Fingerprints
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- **map4** - MinHashed Atom-Pair fingerprint up to 4 bonds
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- Combines atom-pair and ECFP concepts
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- Default: 1024 dimensions
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- Fast and efficient for large datasets
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- **secfp** - SMILES Extended Connectivity Fingerprint
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- Operates directly on SMILES strings
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- Captures both substructure and atom-pair information
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### Extended Reduced Graph
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- **erg** - Extended Reduced Graph
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- Uses pharmacophoric points instead of atoms
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- Reduces graph complexity while preserving key features
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## Pharmacophore Descriptors
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Features based on pharmacologically relevant functional groups and their spatial relationships.
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### CATS (Chemically Advanced Template Search)
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- **cats2D** - 2D CATS descriptors
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- Pharmacophore point pair distributions
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- Distance based on shortest path
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- 21 descriptors by default
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- **cats3D** - 3D CATS descriptors
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- Euclidean distance based
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- Requires conformer generation
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- **cats2D_pharm** / **cats3D_pharm** - Pharmacophore variants
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### Gobbi Pharmacophores
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- **gobbi2D** - 2D pharmacophore fingerprints
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- 8 pharmacophore feature types:
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- Hydrophobic
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- Aromatic
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- H-bond acceptor
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- H-bond donor
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- Positive ionizable
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- Negative ionizable
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- Lumped hydrophobe
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- Good for virtual screening
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### Pmapper Pharmacophores
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- **pmapper2D** - 2D pharmacophore signatures
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- **pmapper3D** - 3D pharmacophore signatures
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- High-dimensional pharmacophore descriptors
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- Useful for QSAR and similarity searching
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## Shape Descriptors
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Descriptors capturing 3D molecular shape and electrostatic properties.
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### USR (Ultrafast Shape Recognition)
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- **usr** - Basic USR descriptors
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- 12 dimensions encoding shape distribution
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- Extremely fast computation
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- **usrcat** - USR with pharmacophoric constraints
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- 60 dimensions (12 per feature type)
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- Combines shape and pharmacophore information
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### Electrostatic Shape
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- **electroshape** - ElectroShape descriptors
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- Combines molecular shape, chirality, and electrostatics
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- Useful for protein-ligand docking predictions
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## Scaffold-Based Descriptors
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Descriptors based on molecular scaffolds and core structures.
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### Scaffold Keys
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- **scaffoldkeys** - Scaffold key calculator
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- 40+ scaffold-based properties
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- Bioisosteric scaffold representation
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- Captures core structural features
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## Graph Featurizers for GNN Input
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Atom and bond-level features for constructing graph representations for Graph Neural Networks.
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### Atom-Level Features
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- **atom-onehot** - One-hot encoded atom features
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- **atom-default** - Default atom featurization including:
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- Atomic number
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- Degree, formal charge
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- Hybridization
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- Aromaticity
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- Number of hydrogen atoms
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### Bond-Level Features
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- **bond-onehot** - One-hot encoded bond features
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- **bond-default** - Default bond featurization including:
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- Bond type (single, double, triple, aromatic)
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- Conjugation
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- Ring membership
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- Stereochemistry
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## Integrated Pretrained Model Collections
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Molfeat integrates models from various sources:
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### HuggingFace Models
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Access to transformer models through HuggingFace hub:
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- ChemBERTa variants
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- ChemGPT variants
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- MolT5
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- Custom uploaded models
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### DGL-LifeSci Models
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Pre-trained GNN models from DGL-Life:
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- GIN variants with different pre-training tasks
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- AttentiveFP models
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- MPNN models
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### FCD (Fréchet ChemNet Distance)
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- **fcd** - Pre-trained CNN for molecular generation evaluation
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### Graphormer Models
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- Graph transformers from Microsoft Research
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- Pre-trained on quantum chemistry datasets
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## Usage Notes
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### Choosing a Featurizer
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**For traditional ML (Random Forest, SVM, etc.):**
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- Start with **ecfp** or **maccs** fingerprints
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- Try **desc2D** for interpretable models
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- Use **FeatConcat** to combine multiple fingerprints
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**For deep learning:**
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- Use **ChemBERTa** or **ChemGPT** for transformer embeddings
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- Use **gin-supervised-*** for graph neural network embeddings
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- Consider **Graphormer** for quantum property predictions
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**For similarity searching:**
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- **ecfp** - General purpose, most popular
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- **maccs** - Fast, good for scaffold hopping
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- **map4** - Efficient for large-scale searches
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- **usr** / **usrcat** - 3D shape similarity
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**For pharmacophore-based approaches:**
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- **fcfp** - Functional group based
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- **cats2D/3D** - Pharmacophore pair distributions
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- **gobbi2D** - Explicit pharmacophore features
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**For interpretability:**
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- **desc2D** / **mordred** - Named descriptors
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- **maccs** - Interpretable substructure keys
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- **scaffoldkeys** - Scaffold-based features
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### Model Dependencies
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Some featurizers require optional dependencies:
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- **DGL models** (gin-*, jtvae): `pip install "molfeat[dgl]"`
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- **Graphormer**: `pip install "molfeat[graphormer]"`
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- **Transformers** (ChemBERTa, ChemGPT, MolT5): `pip install "molfeat[transformer]"`
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- **FCD**: `pip install "molfeat[fcd]"`
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- **MAP4**: `pip install "molfeat[map4]"`
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- **All dependencies**: `pip install "molfeat[all]"`
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### Accessing All Available Models
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```python
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from molfeat.store.modelstore import ModelStore
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store = ModelStore()
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all_models = store.available_models
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# Print all available featurizers
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for model in all_models:
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print(f"{model.name}: {model.description}")
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# Search for specific types
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transformers = [m for m in all_models if "transformer" in m.tags]
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gnn_models = [m for m in all_models if "gnn" in m.tags]
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fingerprints = [m for m in all_models if "fingerprint" in m.tags]
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```
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## Performance Characteristics
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### Computational Speed (relative)
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**Fastest:**
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- maccs
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- ecfp
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- rdkit fingerprints
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- usr
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**Medium:**
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- desc2D
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- cats2D
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- Most fingerprints
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**Slower:**
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- mordred (1800+ descriptors)
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- desc3D (requires conformer generation)
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- 3D descriptors in general
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**Slowest (first run):**
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- Pretrained models (ChemBERTa, ChemGPT, GIN)
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- Note: Subsequent runs benefit from caching
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### Dimensionality
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**Low (< 200 dims):**
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- maccs (167)
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- usr (12)
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- usrcat (60)
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**Medium (200-2000 dims):**
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- desc2D (~200)
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- ecfp (2048 default, configurable)
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- map4 (1024 default)
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**High (> 2000 dims):**
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- mordred (1800+)
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- Concatenated fingerprints
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- Some transformer embeddings
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**Variable:**
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- Transformer models (typically 768-1024)
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- GNN models (depends on architecture)
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