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Drug Discovery — Drug discovery: ChEMBL search, drug-likeness, interactions

Drug Discovery — Drug discovery: ChEMBL search, drug-likeness, interactions

Intermediate11 minutes3 questions2026-08-09
The idea in one minute

Start with meaning, then move to detail.

This lesson explains Drug Discovery — Drug discovery: ChEMBL search, drug-likeness, interactions as part of extending Hermes and connecting external tools. You will learn what it does, when it matters, and the smallest safe test that proves it works.

If you are new

If you are new, do not memorize names. Focus on three questions: what problem does this solve, what access does it need, and how can you verify the result?

For hands-on use

For practice, inspect the first example, identify its effects, run it on test data, and compare the result with the source claim.

For specialists

For advanced readers, inspect Skill metadata, Reference: full SKILL.md, Core Workflows, then verify failure modes and version compatibility.

What do you need first?

Complete installation and one successful task before adding new capabilities.

What will you know?

A clear outcome before you read.

  • Understand Drug Discovery — Drug discovery: ChEMBL search, drug-likeness, interactions without assumed prior knowledge.
  • Separate the source description from what still needs testing in your environment.
  • Read the first command and identify its inputs and outputs before copying it.
Lesson terms

Short definitions before the details.

Skill
An instruction bundle that teaches Hermes a repeatable workflow without necessarily adding an external service.
Official page description

Drug discovery: ChEMBL search, drug-likeness, interactions

Topic map

What does the source say, and in what order?

  1. 01
    Skill metadata

    Start here to understand the core idea or structure.

  2. 02
    Reference: full SKILL.md

    Read this after the foundation, then connect it to the previous step.

  3. 03
    Core Workflows

    Read this after the foundation, then connect it to the previous step.

  4. 04
    1 — Bioactive Compound Search (ChEMBL)

    Read this after the foundation, then connect it to the previous step.

  5. 05
    2 — Drug-Likeness Calculation (Lipinski Ro5 + Veber)

    Read this after the foundation, then connect it to the previous step.

  6. 06
    3 — Drug Interaction & Safety Lookup (OpenFDA)

    Read this after the foundation, then connect it to the previous step.

  7. 07
    4 — PubChem Compound Search

    Read this after the foundation, then connect it to the previous step.

  8. 08
    5 — Target & Disease Literature (OpenTargets)

    Read this after the foundation, then connect it to the previous step.

  9. 09
    Reasoning Guidelines

    Read this after the foundation, then connect it to the previous step.

  10. 10
    Important Notes

    Finish here to verify the result and special cases.

Examples from the official page

Copy only after you understand the effect.

# Search compounds by target name (e.g. "EGFR", "COX-2", "ACE") TARGET="$1" ENCODED=$(python3 -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" "$TARGET") curl -s "https://www.ebi.ac.uk/chembl/api/data/target/search?q=${ENCODED}&format=json" \ | python3 -c " import json,sys data=json.load(sys.stdin) targets=data.get('targets',[])[:5] for t in targets: print(f\"ChEMBL ID : {t.get('target_chembl_id')}\") print(f\"Name : {t.get('pref_name')}\") print(f\"Type : {t.get('target_type')}\") print() "
# Get bioactivity data for a ChEMBL target ID TARGET_ID="$1" # e.g. CHEMBL203 curl -s "https://www.ebi.ac.uk/chembl/api/data/activity?target_chembl_id=${TARGET_ID}&pchembl_value__gte=6&limit=10&format=json" \ | python3 -c " import json,sys data=json.load(sys.stdin) acts=data.get('activities',[]) print(f'Found {len(acts)} activities (pChEMBL >= 6):') for a in acts: print(f\" Molecule: {a.get('molecule_chembl_id')} | {a.get('standard_type')}: {a.get('standard_value')} {a.get('standard_units')} | pChEMBL: {a.get('pchembl_value')}\") "
# Look up a specific molecule by ChEMBL ID MOL_ID="$1" # e.g. CHEMBL25 (aspirin) curl -s "https://www.ebi.ac.uk/chembl/api/data/molecule/${MOL_ID}?format=json" \ | python3 -c " import json,sys m=json.load(sys.stdin) props=m.get('molecule_properties',{}) or {} print(f\"Name : {m.get('pref_name','N/A')}\") print(f\"SMILES : {m.get('molecule_structures',{}).get('canonical_smiles','N/A') if m.get('molecule_structures') else 'N/A'}\") print(f\"MW : {props.get('full_mwt','N/A')} Da\") print(f\"LogP : {props.get('alogp','N/A')}\") print(f\"HBD : {props.get('hbd','N/
Try it now

Read the first command and identify its inputs and outputs before copying it.

Match every command to your installed Hermes version, review the files and accounts it can reach, and use non-sensitive data for the first test. If this explanation differs from the source, the official source wins.

Knowledge check

Three decisions before completion.

1. What is the source of truth when “Drug Discovery — Drug discovery: ChEMBL search, drug-likeness, interactions” changes?
2. What is the best way to apply this lesson?
3. What should happen before a step can modify files or an external account?