Data Characteristics in Target Discovery
Target discovery data originates from public databases (e.g., NCBI Gene, UniProt, DrugBank, OMIM) and internal R&D documents and experimental reports. Data updates frequently. Public databases may update weekly or monthly. Internal data generates in real-time with experimental progress. Document structures are complex. They include unstructured research papers and patent texts, alongside structured gene expression profiles, protein interaction data, and disease model data. Fields and units are highly specific. Examples include gene IDs (e.g., ENSG00000123456), protein sequences (e.g., ATGC...), IC50 values (units nM or µM), disease classification codes (e.g., ICD-10), clinical phenotype descriptions (free text), and various biostatistical metrics (e.g., p-value).
Constraints on Multiturn Conversation and Prompts from These Characteristics
Target discovery data characteristics impose specific requirements on multiturn conversation and prompt design. Diverse data sources and high update frequency necessitate continuous knowledge base synchronization. This ensures the model accesses the latest research. Complex document structures and specialized terminology require prompt design to consider domain vocabulary and contextual relevance. This prevents the model from generating generic responses. High-density information, such as gene IDs and protein sequences, demands accurate identification and citation by the model in multiturn conversations. Misidentification leads to significantly incorrect results. Numerical values with units, like IC50 and p-value, require the model to understand their biological meaning and dimension. The model must perform correct comparisons and inferences in conversations. Free-text clinical phenotype descriptions require strong semantic understanding from the model. This allows extracting key information from vague descriptions and associating it with structured data.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale for this Value |
|---|---|---|
maxContext | 8000 tokens | Target discovery contexts often involve complex biological pathways and experimental designs. A longer conversation history maintains coherence. |
Chunk size (Segment Length) | 500 characters (characters) | Ensures appropriate knowledge base segmentation granularity. This preserves semantic completeness while reducing irrelevant information in a single recall. |
Recall count (Recall Count) | Top 10 entries (top 10 items) | Target discovery involves heterogeneous data from multiple sources. Increasing the recall count helps cover more potentially relevant knowledge points. |
Similarity threshold (Similarity Threshold) | 0.75 | Increases the threshold to ensure recall results are highly relevant to specialized target discovery queries, reducing false positives. |
Rerank result count (Rerank Return Count) | Top 5 entries (top 5 items) | Further selects the most relevant segments from the recall results, optimizing the accuracy of the final answer. |
SYSTEM_PROMPT | See description below | Presets the model's role as an expert in the biomedical field, guiding it to produce professional and rigorous answers. |
SYSTEM_PROMPT Description: The system prompt should explicitly instruct the model to act as a "senior bioinformatics scientist" or "drug development expert." It should emphasize expertise in genes, proteins, disease mechanisms, and drug action mechanisms. It should also require the model to cite specific gene IDs, pathway names, and experimental data sources in its answers, and to pay attention to the accuracy of numerical units.
Three Common Pitfalls
- Symptom: The model fails to correctly associate gene IDs or compound names mentioned in previous turns of a multiturn conversation. Reason:
maxContextis set too low. This causes the model to lose historical information from earlier conversations, preventing it from maintaining long-range dependencies. - Symptom: When a user asks for the IC50 value of a specific target, the model's answer lacks units or provides incorrect dimensions. Reason: The knowledge base's extraction and storage of numerical data did not adequately consider unit information, or the prompt did not explicitly require the model to focus on and output units.
- Symptom: Content from uploaded experimental reports (PDF/DOCX) cannot be effectively retrieved and cited in conversations. Reason: The file parser incompletely extracts text from complex tables or figures. This leads to missing critical information in the knowledge base index.
How to Verify Configuration
- Conduct a series of multiturn conversations involving specific gene IDs, protein sequences, and IC50 values. Verify if the model can correctly identify and cite these entities throughout the conversation.
- Upload an experimental report containing complex tables and figures. Then, ask about key data points from the report (e.g., the expression level of a certain gene under specific conditions). Check if the model can accurately retrieve and reiterate the relevant information.
- Engage in simulated clinical trial pre-screening conversations. Assess if the model can associate potential targets or relevant research from the knowledge base based on user-provided patient characteristics and disease phenotypes, and provide biologically meaningful explanations.
The values provided are common starting points. Measure them 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.