Data Characteristics
Peptide drug data originates from various sources:
- Bioactivity databases (e.g., ChEMBL, PubChem BioAssay)
- Clinical trial registries (e.g., ClinicalTrials.gov)
- Drug approval agency databases (e.g., FDA Orange Book, EMA EPAR)
- Scientific literature
Update frequencies vary. Bioactivity data may update weekly or monthly. Clinical trial and approval data update in real-time based on project progress.
Document structures typically include:
- Peptide sequence (single-letter or three-letter amino acid codes)
- Molecular weight
- Isoelectric point
- Solubility
- Stability
- Target information
- Mechanism of action
- Pharmacokinetic (PK) parameters
- Pharmacodynamic (PD) parameters
- Clinical phase
- Indications
Units involved include:
- Molar concentration (nM, µM)
- Mass (Da, kDa)
- pH value
- Temperature (℃)
- Half-life (h)
- Bioavailability (%)
Constraints for Tool Calling and Plugins
The diversity of peptide sequences and structural data requires tools to handle multiple representation formats. For example, converting amino acid sequences to SMILES or InChI Key, or vice-versa.
The complexity of targets and mechanisms of action necessitates tools capable of querying and comparing protein-interaction networks to identify potential off-target effects.
The numerical nature of PK and PD parameters means tools must perform numerical calculations and comparisons, such as calculating drug half-life or predicting in vivo exposure.
Dynamic updates in clinical trial data require tools to access external databases in real-time, ensuring information timeliness.
Specific data fields, such as molecular weight and solubility, require precise unit conversion and format validation when passed as tool parameters. This prevents call failures due to data type mismatches.
Configuration Suggestions
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 2000 characters | Peptide sequences and related descriptions are often long; this ensures context completeness. |
similarityThreshold | 0.75 | A high threshold ensures retrieved peptide information is highly relevant to the user query. |
toolCallTimeout | 60 seconds | Most bioinformatics queries and calculations are time-consuming; this provides sufficient execution time. |
maxToolRetries | 3 times | Increases call success rate when external APIs experience occasional network fluctuations or temporary unavailability. |
responseSchema | JSON Format | Facilitates subsequent parsing of structured peptide data, such as sequence, molecular weight, and targets. |
parameterSchema | JSON Format | Clearly defines data types and units for tool input parameters, for example, sequence (string), mw (float, Da). |
Common Pitfalls
- Symptom: The AI generates a generic response instead of calling an external tool when peptide sequence information is needed. Reason:
parameterSchemais not precisely defined, preventing the AI from correctly mapping the peptide sequence in the user query to the tool's input parameters. - Symptom: When calling an external tool to retrieve peptide pharmacokinetic data, the half-life field in the returned result is empty. Reason: The field name in the external database does not match the field name defined in
responseSchema, leading to parsing failure. - Symptom: The tool call times out during peptide solubility calculation. Reason: The external calculation service response time is too long, and the
toolCallTimeoutvalue is insufficient to cover its execution time.
Verification Steps
- Construct complex queries containing peptide sequences and targets. Observe if the AI accurately identifies the intent and calls the corresponding tool.
- Use FastGPT's log interface to check if the tool call request parameters align with the
parameterSchemadefinition and if the external tool returns anHTTP status codeof 200. - For different data types of peptide queries (e.g., sequence, molecular weight, clinical phase), verify that the data returned by the tool is complete and that fields are correct.
- Simulate external tool response delays or errors. Observe if FastGPT's error handling mechanism performs retries or fallback strategies as expected.
The values provided are common starting points and should be measured against your own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.