Multiturn Conversation and Prompting for Lead Optimization

Data Characteristics in Lead Optimization

Lead optimization data primarily originates from high-throughput screening reports, structure-activity relationship (SAR) analysis documents, in vitro activity test data, and preliminary pharmacokinetic (PK) and pharmacodynamic (PD) data reports. This data exists in both structured (e.g., compound library database records, experimental result spreadsheets) and unstructured forms (e.g., research logs, project progress reports, meeting minutes). The update frequency is high, with new experimental data potentially generated weekly or bi-weekly. Document structures vary, including PDF experimental reports, Word or Markdown analysis summaries, and CSV or Excel raw data files. Fields and units are highly specialized, for example, IC50 values (unit nM or µM), LogP values, molecular weight (unit g/mol), half-life (unit h or min), and various biological activity indicators.

Constraints Imposed by These Characteristics on Multiturn Conversation and Prompting

High-frequency data streams require the knowledge base to support rapid indexing and incremental updates, ensuring multiturn conversations are based on the latest information. Diverse document structures and a high proportion of unstructured data make file parsing and information extraction critical challenges, necessitating specialized pre-processing workflows. Specialized fields and units demand more rigorous prompt engineering, requiring explicit unit conversions or explanations within the context to prevent ambiguity when the model interprets these technical terms. For instance, for IC50 values, the model must distinguish whether it refers to inhibitory concentration or other similar indicators. Additionally, multiturn conversations may involve cross-document, cross-experimental data queries, requiring the system to accurately identify and extract logical relationships between data from different sources to support complex reasoning.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Experimental reports and high-throughput screening data files are often large; ensure full upload capacity.
`maxContext`	`4000 characters`	Ensure capacity for multiple experimental data segments and SAR analysis content in multiturn conversations.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing complex PDF reports and large CSV files can take a long time.
`Chunk size` (Segment Length)	`800–1200 characters`	Balances semantic completeness of paragraphs and retrieval efficiency, accommodating detailed descriptions in long reports.
`Recall count` (Recall Count)	`Top 10 entries` (Top 10)	Ensures coverage of multiple experimental results and analysis reports, providing sufficient context for complex queries.
`Similarity threshold` (Similarity Threshold)	Calibrate based on actual measurements, suggested `0.75–0.85`	Balances accuracy and recall, especially when technical terms are similar but semantically slightly different.
`Rerank result count` (Rerank Return Count)	`Top 5 entries` (Top 5)	Focuses on the most relevant key information, reducing the burden on the model from processing irrelevant context.

Three Common Pitfalls

Symptom: After uploading a PDF file, data within it cannot be referenced in conversations, or an "file parsing failed" error appears. Reason: The PARSE_FILE_TIMEOUT_SECONDS parameter is set too low, preventing large or complexly formatted files from completing parsing within the allotted time.
Symptom: After multiple turns of conversation, the model provides irrelevant compound structures or activity data. Reason: The maxContext parameter is insufficient, causing critical contextual information mentioned in earlier turns to be truncated, and the model cannot maintain a complete logical chain.
Symptom: Conversation response speed significantly slows down, sometimes showing an Unexpected end of JSON input error. Reason: Insufficient knowledge base index optimization, or high retrieval service load, leading to excessive time spent in data recall and model inference stages, exceeding system or client timeout limits.

How to Confirm Correct Configuration

Upload various types of lead optimization-related files (PDF reports, Excel spreadsheets, Word documents). Check if the file status shows "parsing completed" and verify that file content can be correctly retrieved through questioning.
Conduct multiturn conversation tests. Ask questions involving different experimental data, SAR analysis, and preliminary PK/PD reports. Check if the model can accurately associate and provide consistent responses.
Simulate complex queries from actual registration processes, such as "Please list all compounds with IC50 less than 100 nM and LogP values between 2-3, along with their corresponding half-lives." Check if the model can precisely extract and integrate data from the knowledge base.

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.