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molfeat

Convert molecular structures into machine-learning-ready feature vectors for cheminformatics workflows.

Updated Jul 1, 2026
Search Tags:#virtual screening#cheminformatics#fingerprints#molecular featurization#qsar#molecular embeddings
One-click install
npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill molfeat-jasrajtulsi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: molfeat
Source: https://github.com/jasrajtulsi/GRAD-SCOPE/tree/main/.claude/skills/molfeat
Command: npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill molfeat-jasrajtulsi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Molfeat removes the manual burden of converting chemical structures into numerical representations so you can build models, compare compounds, and explore chemical space faster.

Core Features & Use Cases

  • Fingerprint generation: Create ECFP, MACCS, MAP4, and other fixed-length molecular fingerprints for classic machine learning.
  • Descriptor calculation: Compute RDKit, Mordred, pharmacophore, and shape descriptors for interpretable modeling and analysis.
  • Pretrained embeddings: Use ChemBERTa, ChemGPT, GNN, and Graphormer models to produce learned molecular vectors.
  • Workflow integration: Batch featurize SMILES data, combine multiple featurizers, cache results, and plug outputs into scikit-learn or PyTorch pipelines.
  • Use case: Apply the skill to a QSAR or virtual screening task where you need reliable molecular features from a large list of SMILES strings.

Quick Start

Use the molfeat skill to featurize your SMILES dataset with an appropriate molecular calculator and return a ready-to-model feature matrix.

Frequently Asked Questions about molfeat

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I convert SMILES strings into machine-learning-ready molecular features?▼

You can convert SMILES strings into ML-ready molecular features by featurizing them into numerical vectors using fixed-length fingerprints, descriptors, or pretrained embeddings for cheminformatics modeling.

What is the best way to generate molecular embeddings for QSAR modeling and virtual screening?▼

Generating molecular embeddings for QSAR modeling and virtual screening is best handled by computing RDKit descriptors or using pretrained models like ChemBERTa and Graphormer to produce learned molecular vectors.

Can I use pretrained transformers like ChemGPT to calculate molecular fingerprints?▼

Yes, you can use pretrained transformers like ChemGPT and ChemBERTa to calculate learned molecular embeddings, which extends beyond traditional fixed-length molecular fingerprints like ECFP and MACCS.

Does molecular featurization for PyTorch pipelines require specific Python version compatibility?▼

Molecular featurization for PyTorch pipelines requires Python 3.9-3.10 compatibility, alongside datamol support, to ensure proper integration with scikit-learn and optional DGL frameworks.

How do I batch featurize a large dataset of molecular structures for similarity search?▼

To batch featurize a large dataset of molecular structures for similarity search, you can process multiple SMILES strings simultaneously, cache the resulting feature vectors, and plug them directly into ML pipelines.

What are the limitations of using RDKit descriptors for molecular featurization?▼

Limitations of using RDKit descriptors for molecular featurization include relying on manual feature engineering rather than learned representations, which may require combining multiple featurizers to capture complex chemical space.

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