Lead Compound Screening Pharmacovigilance: Deployment and

Data Characteristics for This Category

Lead compound screening data primarily originates from high-throughput screening reports, in vitro activity test reports, and preliminary toxicity prediction results. This data typically exists in structured table formats. It includes compound SMILES or InChI structures, CAS numbers, screening hit rates, EC50/IC50 values, and preliminary ADMET prediction parameters (e.g., Caco-2 permeability, CYP450 inhibition activity). Data updates are frequent, with bulk imports potentially occurring weekly or monthly based on experimental progress. Document structures often come as PDF or Excel files, where key data is usually embedded in tables or specific text segments. Field names can vary by laboratory or project, for example, Compound_ID, Activity_Value, Predicted_LogP. Units include nanomolar (nM), micromolar (µM), or dimensionless predicted values.

Deployment and Upgrade Constraints Imposed by These Characteristics

The high update frequency of lead compound screening data requires FastGPT to have efficient data ingestion and indexing capabilities during deployment. This prevents data lag from affecting pharmacovigilance decisions. Structured tabular data necessitates configuring precise text parsers to identify and extract compound structures, activity values, and prediction parameters. Non-structured sections of PDF and Excel reports, such as experimental descriptions and methods, require FastGPT's multimodal processing capabilities for content extraction and semantic understanding. The variety of field names requires the knowledge base to consider alias mapping or flexible query mechanisms during construction. The deployment environment needs a stable network connection to handle large data transfers. Upgrades must ensure smooth migration of existing knowledge bases, preventing data loss or index interruption, especially regarding the uniqueness and integrity of core fields like Compound_ID.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Ensures the upload of Excel or PDF reports containing large amounts of screening data.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Accommodates the parsing time for large report files, preventing parsing failures due to timeouts.
`Chunk size`	`800 characters`	Adapts to longer descriptive texts and data table rows in experimental reports, ensuring semantic integrity.
`Recall count`	`10 entries`	Recalls as much relevant compound information as possible for comparison during the preliminary screening phase.
`Similarity threshold`	`0.75`	Balances structural similarity and activity data relevance, improving the hit rate of relevant results.
`Rerank result count`	`5 entries`	Reranks recalled results, prioritizing the most relevant lead compound information.

Three Common Mistakes

After an upgrade, knowledge base query results are incomplete or inaccurate. This can occur if the new indexing mechanism is not fully compatible with the old data structure, leading to some fields not being indexed correctly.
An HTTP 504 Gateway Timeout error appears when importing large screening reports. This usually happens if file parsing or vectorization takes too long, exceeding the timeout settings of the proxy server or FastGPT itself.
Tool calls fail to recognize custom compound structure query tools. This can be due to a mismatch between the schema definition in the tool description and the parameter format expected by the FastGPT model, or incorrect authentication configuration for the tool interface.

How to Verify Correct Configuration

Upload a PDF report containing known lead compound structures and activity data. Query for detailed information about the compound. Check if the returned content includes all key fields.
Perform a bulk data import operation. Observe the task status. Confirm that all files are successfully parsed and indexed, with no timeout or parsing failure records.
Use the FastGPT application interface to ask natural language questions about compounds within specific activity value ranges. Check if the returned results meet expectations. Compare them with original data to verify accuracy.
Test the tool calling function. Ask the model questions about compound structural similarity analysis. Observe if external structure comparison tools are correctly triggered and executed, and if results are returned.

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.