Data Characteristics in this Category
Pharmacovigilance data during lead compound screening primarily originates from high-throughput screening reports, in vitro activity test data, preliminary toxicity prediction model outputs, and literature data. This data updates frequently, potentially with new data generated after each experimental batch. Document structures typically combine structured and semi-structured formats, such as CSV or Excel tables for screening results, and PDF experimental reports. Fields include compound ID, target, activity values (e.g., IC50, EC50), ADMET prediction parameters (e.g., Caco-2 permeability, CYP450 inhibition rate), and potential toxicity signals (e.g., hERG inhibition). Units vary; for example, activity values are often in nM or μM, and ADMET parameters might be logP or percentages, requiring careful standardization.
Constraints Imposed by these Characteristics on Workflow Orchestration
The high update frequency of lead compound screening data necessitates workflow capabilities for automated triggers and rapid responses to prevent information lag. The mix of structured and semi-structured data requires flexible data parsing and extraction components within the workflow, capable of processing tabular data and extracting key toxicity descriptions from unstructured text. The diversity of units for activity values and ADMET parameters demands standardization in data preprocessing modules to ensure accuracy in subsequent comparisons and analyses. Furthermore, the uncertainty of preliminary toxicity prediction results means the workflow needs to integrate confidence evaluation or multi-source cross-validation logic to avoid false positives or negatives, and to provide interfaces for subsequent expert review.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for this Value |
|---|---|---|
File Upload Size Limit | 200 MB | High-throughput screening reports can contain large amounts of data, requiring support for larger file uploads. |
Chunk Length | 500–800 characters | Balances semantic completeness of text with retrieval efficiency, preventing overly fragmented long documents. |
Recall Count | Top 10 | Ensures sufficient potentially relevant information is covered during preliminary screening for AI evaluation. |
Similarity Threshold | 0.75 | A higher threshold reduces noise when matching toxicity signals and structural features. |
Rerank Return Count | Top 3 | Focuses on the most relevant potential toxicity information, improving AI processing efficiency. |
Model Timeout | 600 seconds | Complex toxicity prediction model inference can be time-consuming, requiring ample time. |
Three Common Pitfalls
- AI output content format inconsistency leads to parsing failures in downstream analysis components. This occurs when the workflow does not explicitly specify AI model output format constraints, such as JSON Schema.
- Unreasonable knowledge base long document chunking logic results in loss of key information or insufficient context during retrieval. This happens when the chunking strategy does not account for the chapter structure of lead compound reports (e.g., "Toxicity Results," "ADMET Predictions") by using appropriate delimiters.
- After workflow deployment, external links point to
http://localhost:3000/chat/, preventing external access. This is due to incorrect configuration of FastGPT's deployment domain or port, leading to the generation of local development environment links.
How to Confirm Correct Configuration
- Upload a typical high-throughput screening report (e.g., containing toxicity data for multiple compounds) and check if the knowledge base correctly parses and chunks the text, and if each chunk's content is semantically complete.
- Query for lead compounds known to have specific toxicity signals to verify if the workflow accurately recalls relevant toxicity descriptions and ADMET prediction data, and observe if the AI output format meets expectations.
- Simulate abnormal data input (e.g., a report missing critical activity values) to check if the workflow's error handling mechanism triggers correctly and provides clear failure prompts or alternative processing paths.
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.