Drug Pipeline Search Skill#

This skill converts natural language questions into structured API queries against a pharmaceutical drug database, then presents the results in a readable format.

Workflow#

1. Parse user intent — Extract key entities from the user's question
2. Build query parameters — Map entities to the query schema below
3. Execute the query — Run scripts/search.py
4. Present results — Format and display drug records to the user

Step 1: Extract Keywords#

Identify the following entity types from the user's question:

FieldTypeDescriptionExampledrug_name``dictDrug name(s){"logic": "or", "data": ["pembrolizumab"]}``company``List[str]Sponsor / developer company["Pfizer", "Roche"]``indication``List[str]Disease / indication["lung cancer", "NSCLC"]``target``dictBiological target(s){"logic": "or", "data": ["PD-1", "VEGF"]}``drug_modality``dictDrug modality{"logic": "or", "data": ["Vaccine", "mRNA"]}``drug_feature``dictDrug feature(s){"logic": "or", "data": ["Biologic", "Non-NME"]}``phase``List[str]Development phase(s)["Preclinical", "I", "II", "III", "IV", "Others", "IND", "Suspended", "Approved", "Unknow", "Withdraw from Market", "BLA/NDA"]``route_of_administration``dictRoute of administration (requires exact formatted values){"logic": "or", "data": ["Intravenous (IV)", "Oral (PO)"]}``page_num``intPage index (0-based)0``page_size``intResults per page (1–2000)200

Dict field format:

json

{"logic": "or", "data": ["value1", "value2"]}

• logic controls how multiple values are combined: "or" (any match) or "and" (all must match). Default to "or" unless the user explicitly wants all terms to apply simultaneously.
• data is the list of keyword strings to match.

Type rules:

• company, indication, phase, location → plain List[str]

• drug_name, target, drug_modality, drug_feature, route_of_administration → dict with logic and data

• Default to page_num: 0, page_size: 10 unless the user specifies otherwise

• Prefer English keywords (the database is indexed in English); translate non-English terms

• drug_modality must use exact strings from this set: json

  [
    "Steroids", "Vaccine", "Antisense RNA", "Antibody-Drug Conjugates, ADCs", "Unknown", "Protein Degrader",
    "Monoclonal Antibodies", "mRNA", "Others", "Cell-based Therapies", "Imaging Agents", "Gene Therapy",
    "miRNA", "Polypeptide", "Recombinant Proteins", "Small Molecule", "siRNA/RNAi", "Trispecific Antibodies",
    "Polyclonal Antibodies", "Bi-specific Antibodies", "Glycoconjugates", "Radiopharmaceutical",
    "Nucleic Acid-based", "Carbohydrates"
  ]
  
  

• drug_feature must use exact strings from this set: json

  [
    "505b2", "Bacterial Product", "Biologic", "Biosimilar", "Device", "Fixed-Dose Combination", "Immuno-Oncology",
    "New Molecular Entity (NME)", "Non-NME", "Precision Medicine", "Reformulation", "Specialty Drug", "Viral"
  ]
  
  

• route_of_administration must use exact strings from this set: json

  [
    "Intraarterial", "Intraurethral", "Inhaled", "Intranasal", "Subcutaneous (SQ) - Unspecified", "Transdermal",
    "Intraocular/Subretinal/Subconjunctival", "Subcutaneous (SQ) Injection", "Intrauterine", "Intralymphatic",
    "Intradiscal", "Intra-amniotic", "Intrathecal", "Intracerebral/cerebroventricular", "Intramuscular (IM)",
    "Intraarticular", "Intracochlear", "Surgical Implantation", "Hemoperfusion", "Subcutaneous (SQ) Infusion",
    "Intravitreal", "Intravenous (IV)", "Oral (PO)", "Intradermal", "Percutaneous Catheter/Injection",
    "Intranodal", "Intravesical", "Intracameral", "Intratympanic", "Intratumoral",
    "Sublingual (SL)/Oral Transmucosal", "Intravaginal", "N/A", "Rectal", "Intracavitary",
    "Intra-Cisterna Magna (ICM) Injection", "Injectable - Unspecified", "Intratracheal", "Topical",
    "Instillation", "Intraintestinal", "Submucosal"
  ]
  
  

Step 2: Execute the Query#

bash

python scripts/search.py --params '<JSON string>'

Or using a parameter file:

bash

python scripts/search.py --params-file /tmp/query.json

Add --raw to receive the unformatted JSON response.

Step 3: Interpret Results#

The response contains:

• total_count — total number of matching drugs
• results — current page of drug records, each with name, phase, modality, targets, companies, indication, development progress, etc.

Step 4: Review and Fallback Search Strategies#

If no results are returned, apply the fallback strategies below before giving up. When an initial query returns zero or poor results, try these strategies in order:

Strategy 1 — Drug Name Variant Expansion#

Drug names in the database may use different formats (with/without hyphens, partial codes, aliases). Expand the drug_name field to include common variants and merge deduplicated results.

json

{
  "drug_name": {"logic": "or", "data": ["SHR-A1904", "SHR A1904", "A1904", "SHR1904"]},
  "page_num": 0,
  "page_size": 50
}

Common variant patterns to try:

• Remove or replace hyphens: SHR-A1904 → SHR A1904, SHRA1904
• Strip prefix/suffix: 9MW-2821 → MW-2821, 9MW2821
• Known alias: include trade names or INN alongside internal codes

---

Strategy 2 — Company-First with Application-Layer Filtering#

When drug name matching is unreliable, use the company as the anchor. Fetch a broad set of the company's drugs, then filter by modality/indication/target in post-processing.

json

{
  "company": ["Roche", "Roche Inc"],
  "page_num": 0,
  "page_size": 500
}

After retrieving results, apply local filters:

• modality == "Monoclonal Antibodies"
• indication contains "breast cancer"
• drug_name matches known code pattern

Use this strategy when the drug code is ambiguous or the API match rate is low.

---

Strategy 3 — Broad Target/Modality Search with Post-Filtering#

When neither name nor company is reliable, search by biological target and modality, then narrow results client-side.

json

{
  "target": {"logic": "or", "data": ["CLDN18.2", "Nectin-4", "HER2"]},
  "drug_modality": {"logic": "or", "data": ["Monoclonal Antibodies"]},
  "page_num": 0,
  "page_size": 200
}

After retrieval, filter by company name or drug code pattern using substring matching (e.g. code starts with SHR, 9MW, A166).

Note: If the API supports regex, patterns like (SHR|9MW|A166) can be passed directly in drug_name.data to broaden matching in a single call.

---

Decision Tree#

text

Initial query returns results?
├── Yes → present results
└── No  → Strategy 1: expand drug_name variants
          └── Still no results → Strategy 2: company anchor + local filter
                                 └── Still no results → Strategy 3: target/modality broad search
Any step hits HTTP 429?
└── Pause entire chain 30s → resume from current strategy
    (sleep ≥5s between every request to avoid triggering 429)

---

Conversion Examples#

User: "Find PD-1 antibodies in Phase 3"

json

{
  "target": {"logic": "or", "data": ["PD-1"]},
  "drug_modality": {"logic": "or", "data": ["Monoclonal Antibodies"]},
  "phase": ["III"],
  "page_num": 0,
  "page_size": 30
}

---

User: "Roche bispecific antibodies for lung cancer"

json

{
  "company": ["Roche"],
  "drug_modality": {"logic": "or", "data": ["Bi-specific Antibodies"]},
  "indication": ["lung cancer"],
  "page_num": 0,
  "page_size": 30
}

---

User: "Oral small molecule KRAS G12C inhibitors"

json

{
  "target": {"logic": "or", "data": ["KRAS"]},
  "drug_modality": {"logic": "or", "data": ["Small Molecule"]},
  "route_of_administration": {"logic": "or", "data": ["Oral (PO)"]},
  "page_num": 0,
  "page_size": 30
}

---

User: "Drugs targeting both PD-1 and VEGF"

json

{
  "target": {"logic": "and", "data": ["PD-1", "VEGF"]},
  "page_num": 0,
  "page_size": 30
}

---

User: "Look up pembrolizumab"

json

{
  "drug_name": {"logic": "or", "data": ["pembrolizumab"]},
  "page_num": 0,
  "page_size": 30
}

---

Dependencies#

• Python 3.8+

• requests library ( pip install requests)

• Environment variable NOAH_API_TOKEN — API authentication token (required)

  • Register for a free account at noah.bio to obtain your API key.

---

Security & Packaging Notes#

• This skill only calls NoahAI official HTTPS endpoints under https://www.noah.bio/api/ and does not contact third-party services.
• It requires exactly one environment variable: NOAH_API_TOKEN. Store it in the environment or a local .env file, and never place it inline in commands, chats, or packaged files.
• The token is scoped to read medical public details only and cannot access private user records.
• The skill does not intentionally persist request parameters locally. Any server-side retention is determined by the NoahAI API service and its operational logging policies.
• It does not request persistent or system-level privileges and does not modify system configuration.
• The skill is source-file based (Python scripts only) and does not require runtime installs, package downloads, or external bootstrap steps.