HTTP Interface and External Systems for Hit Compound Screening

Hit compound screening data originates from high-throughput screening (HTS) platforms, virtual screening computational results, and public compound

Data Characteristics for This Category

Hit compound screening data originates from high-throughput screening (HTS) platforms, virtual screening computational results, and public compound activity databases. Data updates are irregular, depending on experimental progress and data release cycles, potentially weekly or monthly. Document structures are complex, including compound structures (SMILES, InChIKey), molecular fingerprints, target information, biological activity data (IC50, EC50, Ki values, etc.), experimental conditions, batch information, and supplier details. Activity data often uses nanomolar (nM) or micromolar (µM) units and may include confidence intervals or standard deviations. Compound structure data is typically stored in SDF or MOL2 file formats, while activity data is often in CSV or Excel spreadsheets.

Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"

The diverse data sources for hit compound screening necessitate that external system integration supports parsing multiple data formats. Irregular update frequencies mean interface designs require periodic polling or webhook callback mechanisms to ensure timely data synchronization. The complexity of compound structure and biological activity data, particularly involving molecular formulas and multi-unit activity values, demands that interfaces correctly encode and decode data during transmission. This prevents loss of structural information or confusion regarding activity value units. Furthermore, the large volume of compound library data places high demands on interface concurrency and transmission efficiency, requiring optimization techniques like batch processing and compressed transmission. The specificity of data fields, such as IC50_nM or SMILES, requires HTTP request parameters and response bodies to accurately map these business fields.

Configuration Guidelines

Configuration ItemRecommended ValueRationale
batchSize500Balances single request data volume and processing efficiency, preventing overload.
timeoutSeconds600 secondsAddresses potential delays from large-scale molecular structure data transfer and processing.
acceptEncodinggzip, deflateReduces data transfer volume, improving efficiency for large compound libraries.
maxConnections10Limits concurrent connections, preventing excessive pressure on external data sources.
retryAttempts3Handles network fluctuations or transient external system failures, improving data synchronization success rates.
parserConfig.SMILES_field"SMILES"Explicitly specifies the key field name for compound structure data, ensuring correct parsing.

Three Common Mistakes

  • Compound structure information appears garbled or missing in the conversation content returned after an API call. This happens when the HTTP request or response Content-Type header is not correctly set to application/json or text/plain; charset=utf-8, leading to character encoding parsing failures.
  • The knowledge base indexing model changes rapidly, leading to inconsistent retrieval results. This occurs when the external system updates its API version or default model without notification, causing FastGPT to use an unexpected model.
  • After importing activity data, retrieved compound activity values have incorrect units or inaccurate numerical values. This happens when unit conversion rules for activity data fields are not explicitly specified in the interface configuration, or the units returned by the external system do not match expectations.

How to Verify Correct Configuration

  • Check FastGPT's API call logs to confirm HTTP request and response status codes are 200 OK, and the response body contains expected key fields like SMILES and IC50_nM.
  • Manually upload an SDF or CSV file containing typical hit compound data in FastGPT, then query the compound's activity information via the chat interface. Verify that the returned structure and values match the original data.
  • Use the external system's API testing tools to simulate FastGPT's HTTP requests. Validate that the returned data format and field content meet integration requirements, especially for molecular structures and biological activity unit representations.

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.