Multiturn Conversations and Prompts for Target Discovery

Data Characteristics

Target discovery data is highly specialized. It includes multi-dimensional information from genomics, proteomics, metabolomics, and phenomics. Data sources are diverse, including public databases (e.g., NCBI Gene, UniProt, DrugBank), research literature, patent information, and high-throughput screening results. Data update frequencies vary. Public databases typically update quarterly or annually, while experimental data generates in real-time based on project progress. Document structures are complex. They often contain large amounts of unstructured text (e.g., paper abstracts, experimental reports) and semi-structured data (e.g., gene expression profiles, protein interaction networks). Fields and units are highly detailed. Examples include gene ID (Entrez Gene ID), protein sequence (FASTA format), half maximal inhibitory concentration (IC50, unit nM), and binding affinity (Kd, unit nM). This data usually includes strict descriptions of experimental conditions.

Constraints on Multiturn Conversations and Prompts

The complexity of target discovery data imposes specific requirements on multiturn conversation and prompt design. First, diverse data sources require robust multi-source information integration. This ensures users receive comprehensive and consistent target information during conversations. Second, high update frequency means the knowledge base must regularly synchronize with the latest research to avoid providing outdated information. A high proportion of unstructured text requires the RAG (Retrieval Augmented Generation) system to accurately extract key information from lengthy documents and support follow-up questions on specific experimental conditions, methods, and results. Specialized fields and units require prompts to guide the model in understanding and distinguishing similar concepts, such as IC50 and EC50. This ensures accurate citation of values and units in responses. The conversation system also needs to handle deep user inquiries about specific genes, proteins, or disease mechanisms and guide users to progressively refine their query scope.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`500–800 characters`	Target discovery literature is information-dense. Shorter segments risk losing context, while longer ones increase irrelevant noise.
`Recall count` (Recall Count)	`8–12 items`	Requires covering multiple data sources and different perspectives of target information to ensure comprehensive answers.
`Similarity threshold` (Similarity Threshold)	`0.78–0.85`	Avoids recalling semantically similar information with significant differences in targets or experimental conditions, improving recall precision.
`Rerank result count` (Rerank Return Count)	`3–5 items`	After reranking, the most relevant information is placed at the forefront, improving model processing efficiency and answer accuracy.
`maxContext`	`3000–4000 tokens`	Target discovery conversations often involve complex concepts and multiple follow-up questions, requiring a longer conversation history.
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Supports users uploading large literature or experimental data reports for system analysis.

Common Mistakes

Frequent "unable to find relevant target information" or missing information in conversations: This may be due to an outdated knowledge base that lacks the latest research or specific database information.
Failed processing of user-uploaded experimental reports or gene sequence files, with "unsupported file format" errors: This may be due to the system not being configured with parsers for common bioinformatics file formats like .fasta or .pdb.
Model confusion or omission of units when reporting IC50 or Kd values: This may be due to prompts not explicitly requiring the model to focus on and output numerical units, or related fields in the knowledge base not being standardized.

How to Verify Configuration

For a specific target, test multiturn follow-up questions about its mechanism of action, related diseases, and marketed drugs. Check the coherence and accuracy of the answers.
Upload target research literature from different sources and in various formats. Check if the system correctly parses the content and extracts key information.
Ask professional questions involving numerical values and units, such as "What is the IC50 of protein X?". Check if the model accurately reports the value and corresponding units like nM.

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.