Data Characteristics
Data for small molecule drug clinical trial pre-screening originates from public drug databases (e.g., PubChem, ChEMBL), clinical trial registries (e.g., ClinicalTrials.gov), research papers, and patent literature. Data update frequencies vary. Drug structure and physicochemical properties remain relatively stable. Clinical trial progress, indications, and side effects may update monthly or weekly. Document structures are primarily semi-structured and unstructured. This includes compound SMILES strings, CAS numbers, activity data tables, trial protocol PDFs, and research report DOCX files. Fields and units are specialized. For example, IC50 values are typically in nanomoles (nM) or micromoles (µM). Other specialized fields include LogP values, molecular weight (Da), and topological polar surface area (TPSA).
Constraints on Deployment and Upgrade
The diverse and specialized nature of small molecule drug data places high demands on the data ingestion module. Semi-structured data requires precise field extraction. Unstructured documents need advanced text parsing to extract key information. Inconsistent data update frequencies necessitate a flexible incremental update mechanism. This avoids full re-indexing and reduces resource consumption. Specialized fields and units, especially those involving chemical structures and biological activity data, require vector embedding models to accurately understand chemical semantics. These models must also support specific structured query formats. Complex document types, such as those containing charts and chemical formulas, challenge the robustness of file parsers. This can lead to loss of critical information or parsing errors.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Clinical trial protocols and reports can be large. This ensures full document upload. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | PDF documents with complex charts and chemical structures take longer to parse. This prevents timeouts. |
Chunk size (Segment Length) | 800–1200 characters (characters) | Retains sufficient context to understand drug mechanisms or trial details. Avoids excessive length which can reduce recall efficiency. |
Recall count (Recall Count) | 15 entries (items) | Ensures coverage of various relevant information, such as mechanism of action, side effects, and indications. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements, e.g., 0.75 or 0.8 | Clinical trial pre-screening requires high accuracy. A low threshold may introduce irrelevant information. |
Rerank result count (Rerank Return Count) | 5 entries (items) | Focuses on the most relevant core information for engineers to make quick decisions. |
Common Pitfalls
nullvalues appear during iterative data processing. This occurs when some data source fields are missing or have inconsistent formats, leading to incorrect parsing.- Large model calls fail after upgrading to FastGPT 9.0, with logs showing a
markererror. This typically indicates incompatibility between old custom model configurations or API keys and the new version's interface. - The file parsing module in Docker-deployed FastGPT 4.9.0 cannot process specific documents, with logs showing a
splitexception. This may be due to missing essential libraries or font files within the Docker container, causing complex files like PDFs to fail parsing.
Verification Steps
- Upload a PDF document containing chemical structure diagrams and activity data tables. Verify that its content is fully parsed and retrievable.
- Query an known drug for its target, clinical indications, and common side effects. Validate the accuracy and completeness of the returned results.
- Simulate a complex query involving multiple data types (SMILES, CAS number, trial ID). Confirm the system correctly understands and recalls relevant information.
The values provided are common starting points and should be measured against the reader's 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.