Data Characteristics for This Category
Lead compound screening data originates from high-throughput screening reports, structure-activity relationship (SAR) study documents, patent applications, early pharmacotoxicology data, and relevant literature. This data has a relatively low update frequency, typically generated incrementally as projects progress. Document structures include experimental methods, results charts, compound structures, activity data, physicochemical properties, and preliminary safety assessments. Key fields include compound ID, CAS number, molecular weight, LogP value, IC50/EC50, selectivity, cytotoxicity data, target, and specific biomarker concentrations. Activity data is often in nanomolar (nM) or micromolar (µM) units. Toxicity data may involve mg/kg or µg/mL.
Constraints Imposed by These Characteristics on "Knowledge Base Retrieval and Recall"
The highly specialized and structured nature of lead compound screening data imposes specific requirements on knowledge base retrieval and recall. First, the large volume of chemical structures and biological activity data requires effective handling of non-textual information during text chunking to avoid context loss due to splitting. Second, the low update frequency means that after knowledge base construction, the focus is on incremental updates and consistency with historical data. Third, precise numerical units and field requirements mean that retrieval results must accurately match specific numerical ranges or provide context with units. For example, when querying "IC50 less than 10nM," recall results should identify and extract relevant compound activity values. Finally, multi-source heterogeneous data requires the knowledge base to have strong multimodal processing potential to handle mixed retrieval needs for text, images (structures), and tabular data.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
Chunk size (Chunk Length) | 800–1200 characters (characters) | Balances the completeness of structured data and experimental descriptions, preventing critical information from being split. |
Chunk Overlap Length (Chunk Overlap Length) | 100 characters (characters) | Ensures continuous context at chunk boundaries, improving retrieval recall rate. |
maxContext | 3000 Tokens | Accommodates sufficient experimental details and compound descriptions, supporting complex queries. |
Recall count (Number of Retrieved Items) | Top 8 entries (top 8) | Considering the screening logic of lead compound screening results, provides more potentially relevant results for the model to evaluate. |
Similarity threshold (Similarity Threshold) | 0.78–0.85 | Balances recall precision and generalization ability, ensuring retrieved results are highly relevant to the query intent. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Provides sufficient parsing time when processing large experimental reports or patent files. |
Three Common Mistakes
- Knowledge base search tests fail to retrieve expected results. The symptom is empty or irrelevant recall items. This can be due to an overly aggressive chunking strategy that splits compound structures, activity data, and descriptive text, leading to incomplete semantics.
- Uploading a large number of PDF documents containing charts leads to training anomalies or stagnation. This is typically because the default file parser has insufficient processing capability for complex layouts, especially embedded chemical structure diagrams and data tables.
- Setting a high
Recall count(Number of Retrieved Items) does not significantly improve model response quality. The symptom is that the model's answers remain generic. This can be due to aSimilarity threshold(Similarity Threshold) set too low, introducing too many low-relevance chunks and diluting effective information.
How to Confirm Correct Configuration
- For typical queries, such as "lead compounds with IC50 less than 50nM for a certain target," test whether retrieval results accurately recall document segments containing relevant compound IDs, activity data, and units.
- Upload a batch of mixed documents containing chemical structure diagrams, experimental data tables, and detailed descriptions. Observe the knowledge base training logs to ensure all documents are successfully chunked and embedded without parsing failures or timeout errors.
- Use FastGPT's retrieval testing feature to input complex questions related to lead compound screening registration and submission. Evaluate the completeness and relevance of the recalled chunks, and adjust the
Similarity threshold(Similarity Threshold) based on actual business scenarios to match the desired recall precision.
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.