molclaw-drug-likeness

Compute QED scores and Lipinski Rule of Five violations for SMILES lists.

28|2|Updated Mar 31, 2026

Search Tags:#cheminformatics #drug-likeness #smiles #qed #lipinski-rule-of-five #molecular-screening

Ready to run

npx skills add https://github.com/InternScience/MolClaw --skill molclaw-drug-likeness

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: molclaw-drug-likeness
Source: https://github.com/InternScience/MolClaw/tree/main/skills/L1_tools/molclaw-drug-likeness
Command: npx skills add https://github.com/InternScience/MolClaw --skill molclaw-drug-likeness

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill automates the calculation of drug-likeness metrics from SMILES strings, providing standardized QED scores and counts of Lipinski Rule of Five violations to support rapid candidate triage and screening.

Core Features & Use Cases

Batch scoring: Accepts lists of SMILES and returns per-molecule metrics including QED and Lipinski violation counts.
Screening & filtering: Enables filtering and ranking of candidate molecules in virtual screening and optimization workflows.
Integration-ready: Designed to be invoked programmatically within agent pipelines for deterministic feature extraction.

Quick Start

Ask the skill to compute QED scores and Lipinski Rule of Five violation counts for the following SMILES: [SMILES1, SMILES2, ...].

Frequently Asked Questions about molclaw-drug-likeness

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

FAQPage Schema

How do I calculate drug-likeness metrics for a list of SMILES strings?▼

To calculate drug-likeness metrics for SMILES strings, you can use a cheminformatics skill that accepts a batch of molecular strings and returns per-molecule QED scores and Lipinski Rule of Five violation counts in a structured list.

Can I batch compute QED scores and Lipinski violations for virtual screening?▼

Yes, you can batch compute QED scores and Lipinski violations for virtual screening by submitting a list of candidate SMILES to an automated scoring tool, which outputs structured metrics suitable for filtering and ranking.

What is the best way to triage candidate molecules using QED and Lipinski rules?▼

The best way to triage candidate molecules using QED and Lipinski rules is to automate the extraction of these drug-likeness metrics from SMILES strings, enabling rapid filtering and ranking within optimization workflows.

Does this drug-likeness calculator require specific cheminformatics libraries?▼

No specific cheminformatics libraries or dependencies are required to use this drug-likeness calculator, as it operates directly on SMILES inputs and returns structured QED and Lipinski violation metrics without external component setup.

Why use QED alongside Lipinski Rule of Five violations for molecular screening?▼

Using QED alongside Lipinski Rule of Five violations for molecular screening provides a standardized drug-likeness profile, combining a quantitative estimate of drug-likeness with a count of rule-based violations for better candidate triage.

When do I need automated drug-likeness scoring in cheminformatics workflows?▼

You need automated drug-likeness scoring in cheminformatics workflows when performing candidate triage, virtual screening, or optimization tasks that require deterministic, programmatic extraction of QED and Lipinski metrics from molecular lists.