Data Characteristics in this Category
CDMO (Contract Development and Manufacturing Organization) clinical trial pre-screening data originates from internal R&D records, production batch reports, third-party CRO feedback, and public drug target and disease databases. This data updates frequently, often in real-time with R&D progress and batch production. Some external databases may update quarterly or monthly. The document structure primarily consists of structured tabular data, including compound structural information, in vitro activity data, animal model data, and ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) data. Unstructured data, such as experimental reports, preclinical research literature, and patent documents, also constitute a significant portion. Fields cover molecular formulas, IC50 values, solubility, bioavailability, adverse event types, and gene expression profiles. Units include nM, mg/kg, logP values, and relative fluorescence units.
Constraints Imposed by these Characteristics on "Deployment and Upgrade"
The high update frequency and heterogeneous nature of CDMO clinical trial pre-screening data require a deployed system with robust data synchronization and ETL capabilities. This ensures pre-screening models always operate on the latest data. Sensitive internal R&D data demands strict data security and permission management, necessitating detailed access control policies during deployment. The presence of unstructured documents means the model requires efficient text embedding and semantic understanding capabilities, potentially needing a separate document parsing service. The diversity of fields and units requires the system to flexibly handle different data types, preventing calculation errors or model biases due to unit mismatches. Furthermore, frequent data updates imply models may need regular retraining or fine-tuning, posing challenges for automated upgrade processes and rollback mechanisms.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1000 MB | Accommodates large experimental reports and gene sequencing data files. |
maxContext | 6000 tokens | Balances semantic understanding of long documents with computational overhead. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Allows time to parse complex PDFs or nested tables. |
Chunk size | 800–1200 characters | Balances contextual completeness with embedding model processing efficiency. |
Recall count | Top 5 entries | Considers relevance for initial screening and subsequent reranking load. |
Similarity threshold | Calibrate based on actual measurements | Adjust according to the distribution characteristics of different compound structures or biological activity data. |
Three Common Mistakes
- AI conversational variables in workflows fail to retrieve code execution output after an upgrade: This usually occurs due to changes in environment variables or library paths in the new version, leading to communication disruption between the sandbox environment and the main application.
fastgpt-sandboximage not found: This often happens when Docker's configured image source has restricted access or a private repository is not correctly configured, preventing the specific image from being pulled.- Local model configuration is complete, but the model cannot be called: This could be due to a mismatch between the model name in the
.env.localfile and the OneAPI channel configuration, or the local model service port not being correctly exposed.
How to Confirm Correct Configuration
- Check data source synchronization logs. Ensure all specified data sources are ingested successfully at the expected frequency. Verify data types and units of key fields.
- Execute a set of pre-screening test cases that include both structured and unstructured data. Verify the system correctly parses documents and extracts key information. Check the completeness and accuracy of extracted fields.
- Simulate a model training or fine-tuning process. Confirm the system can access all necessary datasets and successfully complete the training process, generating a new model version.
- Validate permission control configuration. Log in with different role accounts to confirm access is restricted to authorized data and functions.
The values provided are common starting points. They should be measured against specific 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.