Multi-Turn Conversations and Prompts for Lead Optimization

Data Characteristics

Lead optimization data originates from high-throughput screening (HTS) results, computational chemistry simulation data, and in vitro ADME (Absorption, Distribution, Metabolism, and Excretion) and Toxicology (Tox) reports. This data exists as structured databases (e.g., compound activity databases, ADME/Tox prediction databases) and unstructured documents (e.g., experimental reports, literature reviews). Data updates occur infrequently, typically in batches after new experimental runs. Document structures vary. Structured data fields include Compound ID, IC50 value (unit: nM), Caco-2 permeability (unit: nm/s), and CYP inhibition rate (unit: %). Unstructured reports contain experimental methods, results descriptions, and conclusions.

Constraints from Data Characteristics on Multi-Turn Conversations and Prompts

Lead optimization data diversity challenges multi-turn conversation accuracy. For example, IC50 value can vary under different experimental conditions. The conversation system must understand context and prompt for specific experimental conditions. Unstructured reports contain numerous technical terms and abbreviations. Prompt design requires careful consideration of domain-specific vocabulary recognition and disambiguation. Infrequent data updates mean knowledge base construction must emphasize data recency tags to avoid citing outdated information. Large, interconnected datasets can lead to many conversation turns. The system needs effective context management to prevent information loss or confusion. Standardized field units require explicit unit specification in prompts to ensure accurate numerical parsing.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8` turns	Balances complex queries with system performance, reducing interference from unnecessary historical information.
`temperature`	`0.3–0.5`	Ensures objective and accurate response generation, reducing hallucination risk.
`Segment Length`	`500–800 characters`	Accommodates paragraph lengths in experimental reports and literature, maintaining semantic integrity.
`Recall Count`	`Top 7`	Covers a broader range of relevant information, addressing multi-dimensional query requirements.
`Similarity Threshold`	`0.75`	Improves recall result relevance, filtering out irrelevant or weakly relevant information.
`Rerank Return Count`	`Top 3`	Selects the most relevant information, reducing model processing burden and improving efficiency.

Common Pitfalls

Frequent "cannot copy" or "generated content has no line breaks" in conversations often indicates front-end rendering or clipboard permission issues, leading to abnormal Markdown format parsing.
In "variable reference" mode, advanced parameter settings like temperature disappear. This occurs because the system defaults to specific strategies in this mode to ensure result consistency, not exposing custom adjustment options.
Empty run data in conversation logs after workflow invocation often points to internal module configuration errors or data transfer interruptions within the workflow. For example, if a preceding module outputs an any type parameter, and a subsequent "text content extraction" module fails to parse it correctly, the extraction result becomes undefined.

Validation

Test multi-turn conversations with lead compound queries of varying complexity. Verify if the system accurately understands user intent and provides relevant data. Check if returned compound information, activity data, and ADME/Tox predictions match original data.
Check if the system correctly identifies and provides explanations or related information when handling queries with technical terms and abbreviations. For example, when querying "hERG inhibition," verify if the system links to relevant compound hERG IC50 data.
Simulate actual operations. Attempt to copy returned results in a conversation. Confirm that copied content is complete and correctly formatted, without garbled text or missing line breaks.
Validate whether the model's output responses achieve the expected balance between accuracy and diversity across different temperature settings, especially for scenarios requiring summary answers.

The values provided are common starting points and should be measured against specific 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.