Database and Operations for Lead Optimization R&D Document

Data Characteristics in this Category

Data in the lead optimization phase comes from high-throughput screening reports, structure-activity relationship analysis documents, in-vitro and in-vivo pharmacodynamic data, toxicology prediction reports, and patent literature. This data updates frequently. New experimental results can emerge daily, especially during compound structure iteration. Document formats vary, including structured experimental data tables, semi-structured report texts, and unstructured image spectra. Specific fields include compound SMILES strings, IC50/EC50 values, PK/PD parameters (e.g., Cmax, Tmax, AUC), and ADMET prediction indicators (e.g., solubility, permeability). Units involve micromolar (μM), nanomolar (nM), milligrams per kilogram (mg/kg), among others.

Constraints on Database and Operations from these Characteristics

High-frequency updates require efficient database write and update capabilities to prevent data backlog and parsing delays. Diverse document types, particularly the mix of structured data and unstructured text, challenge the database's heterogeneous data storage and retrieval capabilities. This requires balancing the query efficiency of relational databases with the flexibility of document databases. Special fields like compound structural formulas need dedicated indexing strategies to ensure efficient retrieval based on structural similarity. Numerical values with precise units, such as micromolar and nanomolar, require the database to store floating-point numbers accurately and avoid precision loss during numerical comparisons. Operationally, focus on data consistency validation, incremental update mechanisms, and system stability under high concurrency to support researchers in obtaining the latest optimization results in real time.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`MONGO_URL`	`mongodb://user:pass@host:port/db_name`	Ensure a complete database connection string, including authentication information and port number, to prevent connection failures.
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Lead optimization documents often contain large amounts of spectra and raw data. Single files can be large, so allocate sufficient upload space.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Structural parsing of complex reports can be time-consuming; extend the parsing timeout.
`Chunk size`	`800 characters`	Balance semantic integrity and recall efficiency; avoid over-segmentation or excessively long paragraphs.
`Recall count`	`Top 10 entries`	Improve relevance; ensure enough lead optimization-related snippets are recalled from a large volume of documents.
`Similarity threshold`	`0.75`	Balance recall precision and breadth; effectively filter irrelevant information and focus on core optimization data.

Common Pitfalls

System startup fails with a MongoDB connection error. This might be due to a missing or incorrect port number in MONGO_URL.
External calls to workflow APIs experience frequent request timeouts, indicated by HTTP status code 504. This might be due to an undersized database connection pool, unable to handle high concurrent writes and queries.
After parsing large experimental reports, some field values are empty or malformed. This might be because text parsing rules do not cover all document variations, leading to incomplete structural extraction.

Verification Steps

Use the FastGPT backend connection test feature to confirm that the MongoDB database connects and writes data correctly.
Simulate high concurrency scenarios using API load testing tools. Verify that the workflow API responds stably under the expected concurrency and that response times meet expectations.
Randomly select multiple lead optimization documents in different formats for upload and parsing. Check if key fields like IC50 and SMILES are extracted accurately and if unit conversions are correct.
Retrieve specific compound structures or pharmacodynamic indicators from the knowledge base. Verify the accuracy and relevance of the recall results, ensuring that the similarity threshold and recall count settings are appropriate.

Note: The values given 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.