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drug-discovery

Identify ChEMBL targets and extract active compounds with ADMET and drug-likeness checks.

Updated Aug 23, 2026
Search Tags:#bioactivity#ChEMBL#drug-discovery#ADMET#medicinal-chemistry#lead-optimization
One-click install
npx skills add https://github.com/AVOI-CEO/avoi-agent --skill drug-discovery-avoi-ceo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: drug-discovery
Source: https://github.com/AVOI-CEO/avoi-agent/tree/main/optional-skills/research/drug-discovery
Command: npx skills add https://github.com/AVOI-CEO/avoi-agent --skill drug-discovery-avoi-ceo

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Medicinal chemists and researchers need an integrated, open-data driven workflow to search bioactivity data, evaluate drug-likeness, and interpret ADMET profiles to guide lead optimization.

Core Features & Use Cases

  • Bioactive compound search: query ChEMBL to find targets and retrieve top-active compounds.
  • Drug-likeness & ADMET: compute Lipinski Ro5 metrics, Veber criteria, QED score, TPSA, and synthetic accessibility indicators from public data.
  • Drug interaction & safety: surface potential drug interactions via OpenFDA and literature-backed safety insights via OpenTargets.
  • Use Case: a chemist analyzes a new scaffold by pulling related bioactivities, estimating properties, and drafting a lead optimization plan.

Quick Start

Ask it to analyze a compound by name to retrieve properties and ADMET guidance.

Frequently Asked Questions about drug-discovery

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

FAQPage Schema
How do I extract bioactive compounds from ChEMBL for lead optimization?▼

To extract bioactive compounds from ChEMBL for lead optimization, identify suitable targets and query the database to retrieve top active compounds for medicinal chemistry tasks using open APIs.

What is the best way to assess drug-likeness and ADMET profiles for new scaffolds?▼

Assessing drug-likeness and ADMET profiles involves computing Lipinski Ro5 metrics, Veber criteria, QED score, and TPSA from public data, then interpreting safety insights via OpenTargets for practical lead optimization.

Can I check drug interactions and safety using open APIs like OpenFDA and OpenTargets?▼

Yes, you can check drug interactions and safety using OpenFDA to surface potential drug interactions and OpenTargets to gather literature-backed safety insights for analyzed compounds.

Do I need specialized software to compute synthetic accessibility and Veber criteria?▼

No specialized software is needed to compute synthetic accessibility and Veber criteria, as these drug-likeness metrics are calculated directly from public data sources and open APIs.

What are the limitations of using public data for medicinal chemistry lead optimization?▼

Limitations of using public data for lead optimization include relying on open APIs for accuracy, requiring clear caveats for result formats, and potential gaps in ADMET interaction checks across multiple data sources.

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