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kotakbiasa
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drug-discovery

Identify bioactive compounds and evaluate drug-likeness using ChEMBL and PubChem data.

Updated Aug 23, 2026
Search Tags:#bioactivity#cheminformatics#pubchem#drug-discovery#chembl#admet#lead-optimization
One-click install
npx skills add https://github.com/kotakbiasa/hermes-agent --skill drug-discovery-kotakbiasa
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: drug-discovery
Source: https://github.com/kotakbiasa/hermes-agent/tree/main/optional-skills/research/drug-discovery
Command: npx skills add https://github.com/kotakbiasa/hermes-agent --skill drug-discovery-kotakbiasa

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Identify bioactive compounds and evaluate drug-likeness for medicinal chemistry workflows.

Core Features & Use Cases

  • Bioactive compound search across public databases (ChEMBL, PubChem).
  • Drug-likeness assessment (Ro5 + Veber) and basic ADMET interpretation.
  • Use case: screen a target and compare top candidates for lead optimization, then summarize properties for selection.

Quick Start

Ask the agent to search ChEMBL for a target and retrieve the top bioactive compounds with their 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 identify bioactive compounds for a specific target using ChEMBL and PubChem?▼

To identify bioactive compounds, the workflow queries open data from ChEMBL and PubChem using Python scripts. It retrieves top candidates against a specified target and evaluates their drug-likeness metrics for medicinal chemistry.

What is the best way to evaluate drug-likeness and ADMET properties for lead optimization?▼

The best way to evaluate drug-likeness is applying Lipinski's Rule of Five (Ro5) and Veber rules alongside basic ADMET interpretation. This workflow calculates these metrics to summarize properties and compare top candidates during lead optimization.

Do I need paid APIs to retrieve drug discovery data from public databases?▼

No, you do not need paid APIs to retrieve drug discovery data. The workflow relies exclusively on open data sources like ChEMBL, PubChem, OpenFDA, and OpenTargets, fetching data through public endpoints for reproducible results.

Can I use Python 3 and curl to automate target screening and bioactivity searches?▼

Yes, you can use Python 3 and curl to automate target screening and bioactivity searches. The workflow provides scripts that leverage these tools to fetch data from public endpoints and produce reproducible results without paid APIs.

How does drug-likeness assessment work when comparing compounds for medicinal chemistry?▼

Drug-likeness assessment works by applying Ro5 and Veber rules to compounds retrieved from ChEMBL or PubChem. The workflow evaluates these properties alongside basic ADMET data to summarize candidates for selection in medicinal chemistry.

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