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drug-discovery

Retrieve ChEMBL bioactivity data and calculate drug-likeness metrics.

Updated Aug 23, 2026
Search Tags:#bioactivity#drug-discovery#chembl#chemoinformatics#openfda#admet#lipinski
One-click install
npx skills add https://github.com/matlee0409/cronus --skill drug-discovery-matlee0409
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: drug-discovery
Source: https://github.com/matlee0409/cronus/tree/main/optional-skills/research/drug-discovery
Command: npx skills add https://github.com/matlee0409/cronus --skill drug-discovery-matlee0409

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Automates medicinal chemistry research by retrieving bioactivity data, calculating drug-likeness scores, and interpreting ADMET profiles to accelerate lead discovery and evaluation.

Core Features & Use Cases

  • Bioactivity data retrieval from ChEMBL for targets and molecules.
  • Drug-likeness calculations including Lipinski Ro5, QED, TPSA, and synthetic accessibility.
  • Drug-interaction and safety insights via OpenFDA and OpenTargets context for lead optimization.
  • Use Case: Evaluate a portfolio of candidate molecules for a target and compile a ranked dossier with property summaries and ADMET considerations.

Quick Start

Query a target in ChEMBL and generate a ranked list of candidates with basic drug-likeness metrics.

Frequently Asked Questions about drug-discovery

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

FAQPage Schema
How do I retrieve bioactivity data from ChEMBL for drug discovery targets?▼

To retrieve ChEMBL bioactivity data for drug discovery, you query a specific target to automatically extract molecule interactions and generate a ranked list of candidates with basic drug-likeness metrics.

How do I calculate drug-likeness metrics like Lipinski Ro5 and ADMET profiles for candidate molecules?▼

Calculating drug-likeness metrics like Lipinski Ro5 and ADMET profiles involves evaluating molecular properties such as QED, TPSA, and synthetic accessibility to assess lead viability and safety considerations.

Can I use Python3 and curl to automate medicinal chemistry workflows in Linux and Windows environments?▼

Yes, you can automate medicinal chemistry workflows using Python3 and curl across Linux, macOS, and Windows environments by executing script-driven operations loaded from structured instructions.

What is the best way to interpret drug-drug interaction data from OpenFDA during lead optimization?▼

Interpreting drug-drug interaction data from OpenFDA during lead optimization involves querying safety insights to evaluate potential adverse interactions and refine candidate molecule portfolios.

How do I compile a ranked dossier of candidate molecules with property summaries and ADMET considerations?▼

Compiling a ranked dossier of candidate molecules requires evaluating a portfolio against a target, integrating bioactivity data, drug-likeness scores, and ADMET considerations into property summaries.

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