Multi-Turn Conversations and Prompts for Lead Compound

Data Characteristics in this Domain

Lead compound screening data comes from high-throughput screening experiment reports, compound structure information databases, biological activity data, ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicology) prediction reports, and patent literature. Data update frequencies vary. High-throughput screening reports may be generated intensively during a project cycle. Compound structure databases and patent data update quarterly or annually. Document structures often include PDF or Excel for experiment reports, containing fields like experimental conditions, compound ID, activity values, and inhibition rates. Structure information databases are typically in SDF or SMILES format, recording 2D or 3D compound structures. Biological activity data is often tabular, with fields such as Compound_ID, Target_Protein, IC50 (half maximal inhibitory concentration, in nanomolar nM), and Ki (dissociation constant, in nanomolar nM). ADMET reports provide predicted values like Caco-2 permeability (in cm/s) and CYP enzyme inhibition activity.

Constraints Imposed by These Characteristics on Multi-Turn Conversations and Prompts

The diversity and specialized nature of lead compound screening data impose specific requirements on multi-turn conversation and prompt design. First, heterogeneous data sources require flexible retrieval strategies to extract relevant information from different document formats and origins. Second, specialized terms like IC50, Ki, and ADMET parameters need accurate identification and understanding in conversations for effective information extraction and reasoning. Prompts must guide the model to understand user intent and include explanations and contextual associations for these professional concepts. Additionally, experimental data often includes numerical ranges and units. Prompts must ensure the model correctly parses numerical values and performs comparisons when handling such information. For example, if a user queries "compounds with IC50 less than 100 nM," the model needs to identify the IC50 field, the value 100, and the unit nM, then perform the corresponding filtering operation.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8000` tokens	Accommodates accumulated specialized terms and experimental data context in multi-turn conversations, preventing loss of critical information.
`Chunk size` (Segment Length)	`500–700` characters	Ensures each text segment contains a complete experimental description or compound information, avoiding semantic fragmentation.
`Recall count` (Recall Count)	`10–15` items	Covers more potentially relevant document snippets during the initial recall phase, improving subsequent re-ranking accuracy.
`Similarity threshold` (Similarity Threshold)	`0.75–0.82`	Balances recall and precision, preventing irrelevant experiment reports or compound data from being introduced.
`Rerank result count` (Re-ranked Return Count)	`3–5` items	Focuses on displaying the most relevant compounds or experimental results, reducing the user's screening burden.
`Temperature`	`0.3–0.5`	Reduces model randomness in generating responses, ensuring responses are based on factual data and minimizing hallucinations.

Three Common Mistakes

The error "Cannot read properties of null (reading 'q')" appears in the conversation because the model attempts to access a non-existent conversation state or variable.
When a user asks for compounds within a specific IC50 range, the returned results include data outside the range. This occurs because the prompt does not clearly instruct the model to perform numerical comparison and filtering.
After uploading multiple compound structure files, the system only identifies some files. This happens because UPLOAD_FILE_MAX_SIZE or PARSE_FILE_TIMEOUT_SECONDS configurations are insufficient to handle large-volume file uploads and parsing.

How to Confirm Correct Configuration

Perform multi-turn conversation tests to ensure the model accurately identifies specialized terms like IC50, Ki, and nM, and understands their meaning in queries.
Upload experiment reports containing different structure types and activity data. Verify the system can correctly parse file content and extract key fields such as Compound_ID and Target_Protein.
Simulate a user query for "compounds with IC50 less than 50 nM and good Caco-2 permeability." Check if the returned compound list accurately meets all conditions.
Examine log output to confirm that in complex query scenarios, the maxContext parameter is sufficient to maintain conversation coherence, without semantic drift caused by context truncation.

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.