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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

GPT-style Autoregressive Models

Specialized Transformer Models

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

Molecular Descriptors

Calculators for physico-chemical properties and molecular characteristics.

2D Descriptors

3D Descriptors

Comprehensive Descriptor Sets

Electrotopological Descriptors

Molecular Fingerprints

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

Circular Fingerprints (ECFP-style)

Path-Based Fingerprints

Key-Based Fingerprints

Atom-Pair Fingerprints

Topological Torsion Fingerprints

MinHashed Fingerprints

Extended Reduced Graph

Pharmacophore Descriptors

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

Gobbi Pharmacophores

Pmapper Pharmacophores

Shape Descriptors

Descriptors capturing 3D molecular shape and electrostatic properties.

USR (Ultrafast Shape Recognition)

Electrostatic Shape

Scaffold-Based Descriptors

Descriptors based on molecular scaffolds and core structures.

Scaffold Keys

Graph Featurizers for GNN Input

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

Atom-Level Features

Bond-Level Features

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)

Graphormer Models

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 (molfeat 0.11.0):

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)