Data Characteristics in Target Discovery
Quality documents in target discovery typically include drug mechanisms of action, biomarkers, preclinical study data, in vitro and in vivo experimental reports, and compound structure and activity data. Data sources are diverse, encompassing internal experimental records, collaborative institution reports, and public databases (e.g., PubChem, ChEMBL). Document formats vary, including PDF experimental reports, Word SOPs (Standard Operating Procedures), Excel compound screening data, and entries in structured databases. Update frequencies differ; fundamental SOPs might update annually, while experimental data reports could be generated weekly or even daily. Documents often contain chemical structures, protein sequences, biological activity units like IC50 and EC50, dosage units (e.g., nM, μM, mg/kg), and complex statistical charts.
Constraints on Multi-turn Conversations and Prompts Due to Data Characteristics
The characteristics of target discovery quality documents impose specific requirements on multi-turn conversation and prompt configurations. First, document diversity necessitates robust parsing capabilities for various file formats to ensure complete information extraction. Second, frequently updated experimental data requires efficient index update mechanisms to guarantee the timeliness of conversation results. Complex technical terms and units demand precise semantic understanding from the model to avoid misinterpreting biological activity data or chemical structure information. In multi-turn conversations, users may need to trace relationships between different experimental batches, compound structures, or mechanisms of action, requiring the system to effectively manage context and perform multi-dimensional information retrieval. Additionally, users might need to aggregate or compare specific data, such as inquiring about IC50 differences between various compounds, which requires prompt design to guide the model in performing data analysis tasks.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Ensures the upload of PDF reports containing numerous images and charts. |
Chunk size (Chunk Size) | 800-1200 characters (characters) | Accommodates longer paragraphs in professional documents, ensuring semantic completeness. |
Recall count (Recall Count) | Top 10 entries (top 10) | Increases the probability of recalling relevant snippets from complex documents, improving multi-turn conversation accuracy. |
Similarity threshold (Similarity Threshold) | 0.75-0.8 | Enhances the relevance of recall results for specialized terminology and precise numerical values. |
Rerank result count (Reranked Return Count) | Top 5 entries (top 5) | Improves the quality of final results through reranking, building upon a high recall count. |
maxContext | 4096 tokens | Accommodates multi-turn conversation context, specialized terminology, and data. |
Common Pitfalls
- The dialog box for file upload becomes unresponsive for an extended period or shows a parsing failure. This occurs when the file is too large or contains complex encryption unsupported by the system.
- During multi-turn conversations, the model fails to accurately answer questions about specific compound activity data. This happens because numerical fields in tables were not correctly extracted during document parsing.
- The model omits information when summarizing experimental results. This occurs because the prompt did not explicitly instruct the model to integrate data from all relevant experimental batches.
Verification Steps
- Upload various formats of target discovery documents, including PDF, Word, and Excel, to check if they are all successfully parsed and indexed.
- Ask multi-turn questions about specific compounds and biological activity data (e.g., IC50 values) within the documents to assess if the model can accurately cite and establish connections.
- Construct queries containing complex technical terms and units, and observe if the model's understanding and responses align with expectations.
- Simulate user queries for the latest experimental data on the same target or compound at different times to confirm that the system returns the most current version of information.
The values provided are common starting points and should be measured against your 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.