Data characteristics for this category
Lead optimization data originates from high-throughput screening reports, molecular simulation results, biological activity test data, and compound structure databases. This data updates frequently. New experimental data and analysis reports can be generated weekly or even daily during multi-round iterative optimization. Document structures typically include fields like structural formulas (SMILES, InChI), physicochemical properties (LogP, molecular weight), biological activity (IC50, Ki), and ADMET property predictions (Caco-2 permeability, CYP inhibition). Data is commonly stored in formats such as CSV, JSON, or SDF. Units vary by property; for example, IC50 is often expressed in nM or µM, and molecular weight in Da.
Constraints imposed by these characteristics on "Deployment and Upgrade"
The high update frequency of lead optimization data requires systems with efficient data ingestion and index update mechanisms. This ensures models always infer from the latest information. Complex data types like structural formulas need specific parsers and vectorization methods, which directly impact resource consumption and processing time during knowledge base construction. Diverse fields and units, along with their interrelationships, demand a more rigorous knowledge base Schema design to accurately capture this information. Data volume can also grow rapidly with optimization progress, challenging storage capacity and retrieval performance. During upgrades, focus on data model compatibility to prevent parsing errors or semantic deviations caused by field changes or inconsistent units.
Configuration settings
| Configuration Item | Suggested Value | Rationale for this value |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | High-throughput experimental reports are often large, requiring a sufficient file upload limit. |
maxContext | 4000 | Complex information, including structural formulas and activity data, requires a longer context window. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large files and extracting structured data takes a long time; this prevents timeouts. |
Chunk size | 800–1200 characters | Balances the completeness of information in a single segment with retrieval efficiency, accommodating both structured and unstructured content. |
Recall count | Top 10 entries | Ensures coverage of more relevant lead compound information in complex queries. |
Similarity threshold | Calibrate by actual measurement | Similarity calculations for specific structures and properties require adjustment based on data distribution. |
Three common mistakes
- Frontend page access fails after deployment, and container logs show
URI malformed. This can be caused by incorrectFRONTEND_URLorSERVER_URLenvironment variable configurations, preventing the frontend from correctly identifying the backend address. - After knowledge base data import, retrieval results do not match expectations, and some compound property fields are missing. This can be caused by the data parser failing to correctly identify or extract specific biological activity or ADMET property fields.
- The deployed system administrator password automatically resets to its default value after some time. This can be caused by a password reset script not being disabled or configured, leading the system to revert to default settings under specific conditions.
How to confirm correct configuration
- Attempt to upload a CSV file containing fields such as SMILES strings, IC50 values, and molecular weights. Check if the knowledge base correctly identifies and stores this information.
- Execute a query that includes a specific compound structure (via SMILES) and target activity (e.g., "inhibit EGFR, IC50 < 100 nM"). Check if the returned results are accurate and include detailed properties of relevant compounds.
- Simulate a large-scale data update, such as importing new experimental batch data. Observe the time taken for knowledge base index updates and verify that updated retrieval results include the new data.
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.