molecular-descriptors

Calculate molecular fingerprints and physicochemical descriptors from SMILES using RDKit.

6|2|Updated Jun 11, 2026

Search Tags:#virtual screening #cheminformatics #RDKit #QSAR #molecular descriptors

Ready to run

npx skills add https://github.com/pradyumnasagar/open-research-skills --skill molecular-descriptors

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: molecular-descriptors
Source: https://github.com/pradyumnasagar/open-research-skills/tree/main/skills/chemoinformatics/molecular-descriptors
Command: npx skills add https://github.com/pradyumnasagar/open-research-skills --skill molecular-descriptors

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires rdkit, numpy, pandas, molfeat, mapchiral, mhfp, molpy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the computation of molecular fingerprints and physicochemical descriptors, simplifying the process of feature engineering for AI applications in cheminformatics.

Core Features & Use Cases

Molecular Fingerprinting: Calculates a variety of molecular fingerprints (e.g., ECFP, FCFP, MACCS, RDKit FP, AtomPair) and physicochemical descriptors (e.g., Lipinski, QED, TPSA).
Use Case: For a given molecule, this Skill can compute its drug-likeness and other properties, which is crucial for drug discovery and QSAR studies.

Quick Start

Load the molecular-descriptors skill and calculate the molecular features for the molecule with SMILES 'CCO'.

Frequently Asked Questions about molecular-descriptors

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

FAQPage Schema

How do I calculate molecular fingerprints and physicochemical descriptors for QSAR modeling?▼

Molecular descriptors for virtual screening are computed by automating feature extraction with this Skill, which processes SMILES strings to generate ECFP, MACCS, and physicochemical properties like TPSA and QED for drug discovery.

What molecular fingerprints can I generate using RDKit for cheminformatics?▼

Using RDKit, you can generate a variety of molecular fingerprints including ECFP, FCFP, MACCS, RDKit FP, and AtomPair to represent chemical structures for machine learning and virtual screening applications.

Does this Skill support calculating drug-likeness properties like Lipinski and QED?▼

Yes, this Skill supports calculating drug-likeness properties like Lipinski and QED, along with other physicochemical descriptors such as TPSA, to evaluate molecular suitability for drug discovery and QSAR studies.

Can I use pandas and numpy to process molecular data for AI applications?▼

You can use pandas and numpy to process molecular data for AI applications, as this Skill utilizes these libraries alongside RDKit and molfeat to transform chemical structures into numerical features suitable for machine learning models.

What is the best way to automate feature engineering for cheminformatics tasks?▼

The best way to automate feature engineering for cheminformatics tasks is using this Skill, which systematically calculates molecular fingerprints and physicochemical descriptors from SMILES inputs to generate structured datasets for ML pipelines.