Home / molfeat / references/available_featurizers.md

references/available_featurizers.md

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.

  • 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)

← Back to molfeat