Available Featurizers in Molfeat

Available Featurizers in Molfeat

This document provides a comprehensive catalog of all featurizers available in molfeat, organized by category.

Transformer-Based Language Models

Pre-trained transformer models for molecular embeddings using SMILES/SELFIES representations.

RoBERTa-style Models

• Roberta-Zinc480M-102M - RoBERTa masked language model trained on ~480M SMILES strings from ZINC database
• ChemBERTa-77M-MLM - Masked language model based on RoBERTa trained on 77M PubChem compounds
• ChemBERTa-77M-MTR - Multitask regression version trained on PubChem compounds

GPT-style Autoregressive Models

• GPT2-Zinc480M-87M - GPT-2 autoregressive language model trained on ~480M SMILES from ZINC
• ChemGPT-1.2B - Large transformer (1.2B parameters) pretrained on PubChem10M
• ChemGPT-19M - Medium transformer (19M parameters) pretrained on PubChem10M
• ChemGPT-4.7M - Small transformer (4.7M parameters) pretrained on PubChem10M

Specialized Transformer Models

• MolT5 - Self-supervised framework for molecule captioning and text-based generation

Graph Neural Networks (GNNs)

Pre-trained graph neural network models operating on molecular graph structures.

GIN (Graph Isomorphism Network) Variants

All pre-trained on ChEMBL molecules with different objectives:

• gin-supervised-masking - Supervised with node masking objective
• gin-supervised-infomax - Supervised with graph-level mutual information maximization
• gin-supervised-edgepred - Supervised with edge prediction objective
• gin-supervised-contextpred - Supervised with context prediction objective

Other Graph-Based Models

• JTVAE_zinc_no_kl - Junction-tree VAE for molecule generation (trained on ZINC)
• Graphormer-pcqm4mv2 - Graph transformer pretrained on PCQM4Mv2 quantum chemistry dataset for HOMO-LUMO gap prediction

Molecular Descriptors

Calculators for physico-chemical properties and molecular characteristics.

2D Descriptors

• desc2D / rdkit2D - 200+ RDKit 2D molecular descriptors including:
  • Molecular weight, logP, TPSA
  • H-bond donors/acceptors
  • Rotatable bonds
  • Ring counts and aromaticity
  • Molecular complexity metrics

3D Descriptors

• desc3D / rdkit3D - RDKit 3D molecular descriptors (requires conformer generation)
  • Inertial moments
  • PMI (Principal Moments of Inertia) ratios
  • Asphericity, eccentricity
  • Radius of gyration

Comprehensive Descriptor Sets

• mordred - Over 1800 molecular descriptors covering:
  • Constitutional descriptors
  • Topological indices
  • Connectivity indices
  • Information content
  • 2D/3D autocorrelations
  • WHIM descriptors
  • GETAWAY descriptors
  • And many more

Electrotopological Descriptors

• estate - Electrotopological state (E-State) indices encoding:
  • Atomic environment information
  • Electronic and topological properties
  • Heteroatom contributions

Molecular Fingerprints

Binary or count-based fixed-length vectors representing molecular substructures.

Circular Fingerprints (ECFP-style)

• ecfp / ecfp:2 / ecfp:4 / ecfp:6 - Extended-connectivity fingerprints
  • Radius variants (2, 4, 6 correspond to diameter)
  • Default: radius=3, 2048 bits
  • Most popular for similarity searching
• ecfp-count - Count version of ECFP (non-binary)
• fcfp / fcfp-count - Functional-class circular fingerprints
  • Similar to ECFP but uses functional groups
  • Better for pharmacophore-based similarity

Path-Based Fingerprints

• rdkit - RDKit topological fingerprints based on linear paths
• pattern - Pattern fingerprints (similar to MACCS but automated)
• layered - Layered fingerprints with multiple substructure layers

Key-Based Fingerprints

• maccs - MACCS keys (166-bit structural keys)
  • Fixed set of predefined substructures
  • Good for scaffold hopping
  • Fast computation
• avalon - Avalon fingerprints
  • Similar to MACCS but more features
  • Optimized for similarity searching

Atom-Pair Fingerprints

• atompair - Atom pair fingerprints
  • Encodes pairs of atoms and distance between them
  • Good for 3D similarity
• atompair-count - Count version of atom pairs

Topological Torsion Fingerprints

• topological - Topological torsion fingerprints
  • Encodes sequences of 4 connected atoms
  • Captures local topology
• topological-count - Count version of topological torsions

MinHashed Fingerprints

• map4 - MinHashed Atom-Pair fingerprint up to 4 bonds
  • Combines atom-pair and ECFP concepts
  • Default: 1024 dimensions
  • Fast and efficient for large datasets
• secfp - SMILES Extended Connectivity Fingerprint
  • Operates directly on SMILES strings
  • Captures both substructure and atom-pair information

Extended Reduced Graph

• erg - Extended Reduced Graph
  • Uses pharmacophoric points instead of atoms
  • Reduces graph complexity while preserving key features

Pharmacophore Descriptors

Features based on pharmacologically relevant functional groups and their spatial relationships.

CATS (Chemically Advanced Template Search)

• cats2D - 2D CATS descriptors
  • Pharmacophore point pair distributions
  • Distance based on shortest path
  • 21 descriptors by default
• cats3D - 3D CATS descriptors
  • Euclidean distance based
  • Requires conformer generation
• cats2D_pharm / cats3D_pharm - Pharmacophore variants

Gobbi Pharmacophores

• gobbi2D - 2D pharmacophore fingerprints
  • 8 pharmacophore feature types:
    • Hydrophobic
    • Aromatic
    • H-bond acceptor
    • H-bond donor
    • Positive ionizable
    • Negative ionizable
    • Lumped hydrophobe
  • Good for virtual screening

Pmapper Pharmacophores

• pmapper2D - 2D pharmacophore signatures
• pmapper3D - 3D pharmacophore signatures
  • High-dimensional pharmacophore descriptors
  • Useful for QSAR and similarity searching

Shape Descriptors

Descriptors capturing 3D molecular shape and electrostatic properties.

USR (Ultrafast Shape Recognition)

• usr - Basic USR descriptors
  • 12 dimensions encoding shape distribution
  • Extremely fast computation
• usrcat - USR with pharmacophoric constraints
  • 60 dimensions (12 per feature type)
  • Combines shape and pharmacophore information

Electrostatic Shape

• electroshape - ElectroShape descriptors
  • Combines molecular shape, chirality, and electrostatics
  • Useful for protein-ligand docking predictions

Scaffold-Based Descriptors

Descriptors based on molecular scaffolds and core structures.

Scaffold Keys

• scaffoldkeys - Scaffold key calculator
  • 40+ scaffold-based properties
  • Bioisosteric scaffold representation
  • Captures core structural features

Graph Featurizers for GNN Input

Atom and bond-level features for constructing graph representations for Graph Neural Networks.

Atom-Level Features

• atom-onehot - One-hot encoded atom features
• atom-default - Default atom featurization including:
  • Atomic number
  • Degree, formal charge
  • Hybridization
  • Aromaticity
  • Number of hydrogen atoms

Bond-Level Features

• bond-onehot - One-hot encoded bond features
• bond-default - Default bond featurization including:
  • Bond type (single, double, triple, aromatic)
  • Conjugation
  • Ring membership
  • Stereochemistry

Integrated Pretrained Model Collections

Molfeat integrates models from various sources:

HuggingFace Models

Access to transformer models through HuggingFace hub:

• ChemBERTa variants
• ChemGPT variants
• MolT5
• Custom uploaded models

DGL-LifeSci Models

Pre-trained GNN models from DGL-Life:

• GIN variants with different pre-training tasks
• AttentiveFP models
• MPNN models

FCD (Fréchet ChemNet Distance)

• fcd - Pre-trained CNN for molecular generation evaluation

Graphormer Models

• Graph transformers from Microsoft Research
• Pre-trained on quantum chemistry datasets

Usage Notes

Choosing a Featurizer

For traditional ML (Random Forest, SVM, etc.):

• Start with ecfp or maccs fingerprints
• Try desc2D for interpretable models
• Use FeatConcat to combine multiple fingerprints

For deep learning:

• Use ChemBERTa or ChemGPT for transformer embeddings
• Use gin-supervised-* for graph neural network embeddings
• Consider Graphormer for quantum property predictions

For similarity searching:

• ecfp - General purpose, most popular
• maccs - Fast, good for scaffold hopping
• map4 - Efficient for large-scale searches
• usr / usrcat - 3D shape similarity

For pharmacophore-based approaches:

• fcfp - Functional group based
• cats2D/3D - Pharmacophore pair distributions
• gobbi2D - Explicit pharmacophore features

For interpretability:

• desc2D / mordred - Named descriptors
• maccs - Interpretable substructure keys
• scaffoldkeys - Scaffold-based features

Model Dependencies

Some featurizers require optional dependencies:

• DGL models (gin-*, jtvae): pip install "molfeat[dgl]"
• Graphormer: pip install "molfeat[graphormer]"
• Transformers (ChemBERTa, ChemGPT, MolT5): pip install "molfeat[transformer]"
• FCD: pip install "molfeat[fcd]"
• MAP4: pip install "molfeat[map4]"
• All dependencies: pip install "molfeat[all]"

Accessing All Available Models

from molfeat.store.modelstore import ModelStore

store = ModelStore()
all_models = store.available_models

# Print all available featurizers
for model in all_models:
    print(f"{model.name}: {model.description}")

# Search for specific types
transformers = [m for m in all_models if "transformer" in m.tags]
gnn_models = [m for m in all_models if "gnn" in m.tags]
fingerprints = [m for m in all_models if "fingerprint" in m.tags]

Performance Characteristics

Computational Speed (relative)

Fastest:

• maccs
• ecfp
• rdkit fingerprints
• usr

Medium:

• desc2D
• cats2D
• Most fingerprints

Slower:

• mordred (1800+ descriptors)
• desc3D (requires conformer generation)
• 3D descriptors in general

Slowest (first run):

• Pretrained models (ChemBERTa, ChemGPT, GIN)
• Note: Subsequent runs benefit from caching

Dimensionality

Low (< 200 dims):

• maccs (167)
• usr (12)
• usrcat (60)

Medium (200-2000 dims):

• desc2D (~200)
• ecfp (2048 default, configurable)
• map4 (1024 default)

High (> 2000 dims):

• mordred (1800+)
• Concatenated fingerprints
• Some transformer embeddings

Variable:

• Transformer models (typically 768-1024)
• GNN models (depends on architecture)