Lead Optimization Data Structure
Lead optimization data originates from high-throughput screening, in vitro activity tests, and computational chemistry simulations. The core data revolves around compound structures, biological activity (e.g., IC50, EC50), predicted ADMET (absorption, distribution, metabolism, excretion, toxicity) properties, and synthesis route information. Data updates frequently, especially during multi-round optimization iterations, as new compound synthesis and test results continuously flow in. Document structures typically include compound ID, SMILES or InChI structural formulas, CAS numbers, target endpoints, numerical values and units for various activity indicators (e.g., nM, µM), and experimental condition descriptions. Field names often follow IUPAC nomenclature or industry conventions. Unit consistency is crucial for subsequent analysis.
Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"
The high update frequency of lead optimization data requires HTTP interfaces to have efficient data synchronization mechanisms. This ensures the AI Agent accesses the latest experimental results. The unique nature of structural data (SMILES) demands extra attention to encoding and format validation during data transmission and parsing. This prevents structural errors due to character escaping or truncation. Diverse activity indicators and unit fields require flexible handling of different numerical types and unit conversions in interface design. This guarantees data accuracy. Due to potentially large data volumes, especially when iterating through compound libraries, high demands are placed on interface request frequency, concurrency handling capabilities, and response time. This avoids becoming a bottleneck in the research and development process. Error handling mechanisms need refinement to distinguish specific issues such as structure parsing failures, missing activity values, or unit mismatches.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
requestTimeout | 600 seconds | Lead optimization data processing may involve complex calculations or large-scale data retrieval. This ensures sufficient time for request completion. |
maxConnections | Calibrate by actual measurement | Optimize concurrent connections based on the external system's concurrency handling capability and the FastGPT deployment environment resources. |
header.Content-Type | application/json | Data exchange in the biomedical field often uses JSON format, which facilitates structured data transmission. |
body.compound_id_field | SMILES | Use a standardized compound structure identifier as the primary key for queries or updates. This ensures uniqueness. |
error_retry_count | 3 | Provide a limited number of automatic retries for network fluctuations or transient loads on external systems. This improves stability. |
data_parse_strategy | JSONPath | Precisely extract required activity and ADMET fields from complex JSON structures using JSONPath. |
Three Common Mistakes
- An HTTP interface call returns status code 500, but the logs lack clear error information. This makes troubleshooting difficult. The cause is usually an internal logic error in the external system that does not return detailed error information in the response body.
- The AI Agent queries compound activity, but the numerical values in the results are empty or the units are incorrect. This happens when the external interface's returned JSON field names do not match expectations, or different units are not standardized.
- Frequent data synchronization requests cause the external system to slow down or reject connections. This is often due to not setting appropriate request rate limits or concurrent connection numbers, leading to external system overload.
How to Verify Configuration
- Manually send query requests to the HTTP interface for a core compound ID. Verify that key fields like SMILES and IC50 in the returned data match expected values exactly.
- Simulate a data update operation, such as modifying a compound's predicted ADMET value. Immediately verify that the data has been synchronized and updated through a query interface.
- Create a test set in the FastGPT knowledge base with lead optimization-related questions. Query it through the AI Agent and observe if it accurately references the latest data from the external system.
- Monitor HTTP interface response times and error logs. Ensure acceptable response times during peak periods and no persistent connection errors.
The values provided are common starting points. Measure them against your 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.