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drug-discovery

Search ChEMBL for bioactive compounds and calculate drug-likeness.

2|Updated Jun 8, 2026
Search Tags:#bioinformatics#drug discovery#cheminformatics#pharmacology#ADMET analysis
One-click install
npx skills add https://github.com/vikrant-project/devil-agent-ai-platform --skill drug-discovery-vikrant-project
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: drug-discovery
Source: https://github.com/vikrant-project/devil-agent-ai-platform/tree/main/agent_core/optional-skills/research/drug-discovery
Command: npx skills add https://github.com/vikrant-project/devil-agent-ai-platform --skill drug-discovery-vikrant-project

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires curl, python3, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines pharmaceutical research and drug discovery workflows by providing access to bioactive compounds, drug-likeness calculations, and drug-drug interactions.

Core Features & Use Cases

  • Bioactive Compound Search: Search ChEMBL for compounds by target, activity, or molecule name.
  • Drug-Likeness Calculation: Calculate Lipinski Ro5 and Veber oral bioavailability rules for molecules.
  • Drug Interaction & Safety Lookup: Lookup drug-drug interactions and adverse events via OpenFDA.
  • ADMET Analysis: Interpret ADMET profiles and assist with lead optimization.
  • Use Case: A medicinal chemist can use this Skill to analyze potential drug candidates and identify bioactive compounds.

Quick Start

Search for bioactive compounds by target name, such as "EGFR".

Frequently Asked Questions about drug-discovery

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

FAQPage Schema
How do I search for bioactive compounds by target name in ChEMBL?▼

To search bioactive compounds in ChEMBL, query by target name, activity, or molecule name. This automates retrieving bioactive compound data for pharmaceutical research workflows.

What is drug-likeness calculation using Lipinski Ro5 and Veber rules?▼

Drug-likeness calculation evaluates Lipinski Ro5 and Veber oral bioavailability rules for molecules. It assesses chemical properties to determine if a compound is a viable oral drug candidate.

Do I need python3 and curl installed to retrieve drug-drug interactions via OpenFDA?▼

Yes, retrieving drug-drug interactions and adverse events via OpenFDA requires python3 and curl installed. These dependencies execute the API requests for pharmaceutical safety lookups.

Can I interpret ADMET profiles and assist with lead optimization using this approach?▼

Yes, ADMET analysis interprets absorption, distribution, metabolism, excretion, and toxicity profiles. This assists medicinal chemists with lead optimization during drug discovery workflows.

What is the best way to lookup drug adverse events from OpenFDA data?▼

The best way to lookup drug adverse events is querying the OpenFDA API. This retrieves drug-drug interactions and safety data to streamline pharmaceutical research.

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