Data Characteristics in this Category
Lead optimization data originates from high-throughput screening, ADMET prediction models, molecular dynamics simulations, and preliminary in vitro/in vivo efficacy and toxicology reports. Data exists in both structured (e.g., compound library SDF files, activity value CSVs, ADMET prediction JSONs) and unstructured formats (e.g., experimental logs, research report PDFs, image data). Data updates frequently, especially during compound iteration and experimental validation phases, with new data potentially generated daily. Document structures are complex. For example, research reports may contain multiple chapters covering experimental methods, result charts, and statistical analyses. Fields include compound ID, molecular structure, IC50 values, Cmax, T1/2, and toxicity levels. Units are diverse, such as molar concentration (nM), time (hours), and biological activity (% inhibition rate), requiring precise identification and conversion.
Constraints Imposed by these Characteristics on Tool Calling and Plugins
The high update frequency of lead optimization data requires tool calling to support real-time or near real-time data synchronization. This ensures registration documents reflect the latest research progress. Data type diversity, especially the mix of structured and unstructured data, means plugins must support parsing and processing multiple data formats. Examples include extracting specific experimental results from PDF reports or parsing molecular structure information from SDF files. The complexity of fields and units requires plugins to standardize and normalize data after extraction. For instance, activity units from different experimental reports must be unified to meet regulatory submission specifications. Furthermore, sensitive compound information and experimental data necessitate higher security, data isolation, and permission control for tool calling. Plugins processing this data must integrate with specialized computational chemistry or bioinformatics tools, such as calling ChemAxon MarvinSketch for structure visualization or DOCK suite for molecular docking simulations, to generate comprehensive submission support materials.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 20000 characters | Ensures coverage of a complete experimental report or multiple key data points, preventing information truncation. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Accounts for the time required to parse large experimental report PDFs or SDF files, providing sufficient processing time. |
HTTP_REQUEST_TIMEOUT_SECONDS | 60 seconds | Balances responsiveness and stability for external professional tool API calls, avoiding prolonged waits. |
toolCallMaxIterations | 5 | Addresses scenarios in complex submission document generation that may require multiple tool calls and result iterations. |
embeddingModel | text-embedding-ada-002 | Suitable for semantic understanding of biomedical texts, improving relevance recall. |
chunkSize | 800 characters | Balances semantic completeness and vector retrieval efficiency, especially for lengthy research reports. |
Three Common Pitfalls
- Symptom: Plugin calls to external APIs return
HTTP 502 Bad Gatewayerrors. Cause: External API service instability or network configuration issues prevent FastGPT from successfully forwarding requests. - Symptom: Key fields (e.g., IC50 values) extracted from PDF documents are empty or incorrectly formatted. Cause: Complex PDF document structure, where the plugin's OCR or text parsing rules fail to accurately identify the target field's location and pattern, or unit conversion logic is flawed.
- Symptom: After referencing a plugin in a workflow, generated submission documents do not align with the latest experimental data. Cause: Data synchronization mechanisms are not effectively triggered, or plugin caches are not updated promptly, leading to the use of outdated data.
Verification Steps
- Observe plugin call logs in the FastGPT debugging interface to confirm that external tool API requests and response status codes are all
HTTP 200 OK. - For typical lead compound experimental report PDFs, manually upload and run the parsing plugin. Verify that the output structured data matches the original document content, especially for key numerical values and units.
- Simulate a data update scenario by modifying a compound's activity data. Then, re-run the workflow to generate submission documents and check if the output reflects the latest data changes.
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.