HTTP Interface and External Systems for Lead Compound

Data Characteristics in This Category

Lead compound screening data comes primarily from high-throughput screening reports, compound structure databases, and preliminary in vitro activity test results. This data is typically structured or semi-structured, found in SDF files, CSV files, Excel spreadsheets, or JSON API responses. Data update frequency varies with research progress, ranging from daily updates for high-throughput screening to weekly or monthly for preliminary activity data. Document structures include compound ID, CAS number, molecular formula, SMILES string, activity values (e.g., IC50, EC50), toxicity prediction values (e.g., LD50), pharmacokinetic parameters (e.g., logP, PSA), and experimental condition descriptions. Activity values are often in nanomolar (nM) or micromolar (µM), while toxicity prediction values may use milligrams per kilogram (mg/kg) or molar concentration (M).

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

The highly structured nature of lead compound screening data requires HTTP interfaces to accurately map fields and support parsing of different data formats. For example, multiple records within SDF files and their internal key-value structures necessitate interface capabilities for multi-record processing and custom field parsing. The variety of units for activity and toxicity prediction values means the interface may need pre-set unit conversion rules or a dedicated unit field upon data reception to prevent confusion. Large volumes of high-throughput screening data with frequent updates demand high throughput and stability from HTTP interfaces, requiring support for batch data uploads or streaming data processing. Additionally, if API responses include compound structure information, ensure correct encoding, such as URL encoding or Base64 encoding, to avoid parsing errors caused by special characters.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale for Recommendation
`max_connections`	`50`	Handles high concurrency requests from high-throughput screening data, maintaining system stability.
`request_timeout`	`60 seconds`	Allows sufficient time for processing large SDF files or complex structural data parsing.
`response_format`	`JSON`	Facilitates structured data parsing and integration with downstream systems, supporting various data types.
`data_encoding`	`UTF-8`	Ensures correct transmission and display of text fields like compound names and descriptions.
`batch_size`	`1000`	Balances data volume per request with processing efficiency, reducing network overhead.
`parser_plugins`	`SDFParser, CSVParser`	Supports automatic identification and parsing of various lead compound data source formats.

Three Common Pitfalls

HTTP requests return a 400 Bad Request error, with data fields being empty or incorrectly formatted. This happens when the sent JSON or XML data structure does not match the interface's expectations, or when unit fields are not correctly passed, leading to server-side parsing failures.
Interface calls succeed, but the returned activity values do not match expectations, showing significant numerical discrepancies. This can result from unit conversion errors. For example, if the interface defaults to processing micromolar data, but nanomolar data is provided, the result may be incorrectly magnified or reduced by 1000 times.
When uploading large amounts of data, the interface returns a 504 Gateway Timeout error. This occurs when the data volume in a single request exceeds the request_timeout limit, causing proxy servers or gateways to close the connection before backend processing completes.

How to Verify Configuration

Use Postman or similar tools to simulate various typical requests with different data formats (e.g., JSON containing SMILES strings, SDF snippets). Check if the interface returns a status_code of 200 and verify the completeness and accuracy of the response data fields.
Send activity value data with different units (e.g., nM, µM). Check if the processed or converted activity values in the response match expectations and confirm correct parsing of unit fields.
During peak times or simulated high-concurrency scenarios, continuously call the interface and monitor server resource (CPU, memory) usage. Ensure stable system operation and the absence of request_timeout or connection pool exhaustion errors.

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.