Data Characteristics
Bispecific antibody (BsAb) R&D documents originate from diverse sources. These include patent applications, clinical trial reports, literature reviews, internal experimental records, and sequence databases. Document update frequencies vary. Patents and clinical trial reports typically have fixed publication cycles, while internal experimental records update in real time. Document structures are diverse, ranging from highly structured database entries to free-text scientific papers. Key fields include antibody sequences (heavy chain, light chain variable region CDRs), target information (antigen name, Uniprot ID), mechanism of action, affinity data (Kd value, EC50 value), manufacturing processes, pharmacokinetic parameters, toxicity data, and clinical indications. Affinity units are commonly nanomolar (nM) or picomolar (pM). Dosage units involve milligrams per kilogram (mg/kg) or micrograms per kilogram (µg/kg).
Constraints Imposed by These Characteristics on Multiturn Conversation and Prompts
The complexity of bispecific antibody R&D documents places specific requirements on designing multiturn conversations and prompts. Frequently updated internal experimental records require the model to quickly learn and integrate new knowledge, with the conversation system reflecting the latest advancements promptly. Diverse document structures mean prompts must effectively guide the model to extract information from different text types. Examples include identifying key sequences from free text or extracting affinity data from tables. Multiturn conversations need context maintenance to link antibody sequences with targets, and mechanisms of action with clinical indications during follow-up questions. The specificity of fields and units requires prompts to clearly state the data type to extract and the expected unit format, preventing model confusion. For instance, when a user asks "What is the affinity of antibody X?", the system should distinguish between Kd and EC50 and present the value in the correct nM or pM unit.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8 | R&D conversations often require longer contexts to track antibody design, experimental results, and potential clinical applications. 8 turns cover most scenarios. |
Chunk size | 800–1200 characters | Key information density is high in bispecific antibody documents. Too short segments risk semantic fragmentation; too long segments increase irrelevant information interference. |
Recall count | Top 5 entries | Ensures that multiple key fragments relevant to complex queries are recalled from a large volume of documents, increasing information coverage. |
Similarity threshold | 0.75 | For biological sequences and pharmacological data, a higher similarity threshold is needed to ensure the precision of recalled content and reduce false positives. |
Rerank result count | 3 | Selects the 3 most relevant pieces of information for detailed analysis, preventing the model from processing excessive redundant information and improving response efficiency. |
Prompt Template | Calibrate by actual measurement | Customize prompt templates for different query types (e.g., sequence queries, mechanism queries) to guide the model in precisely extracting specific fields and units. |
Common Pitfalls
- Conversation interruption or irrelevant responses: This occurs when
maxContextis set too short, causing the model to forget antibody names or target information mentioned in earlier turns. - Inconsistent affinity data units extracted: This happens when the required units are not explicitly specified in the prompt, or the model fails to correctly identify unit conversions when processing multi-source documents.
- Generic information returned for specific sequence queries: This is due to
Similarity thresholdbeing set too low, recalling many document fragments not directly related to the queried sequence.
Validation Steps
- Conduct multiturn conversation tests. Ensure the model accurately associates and answers questions about antibodies or targets from earlier turns, even in the 7th-8th turn.
- Randomly select 10 queries containing affinity data. Verify that the numerical values returned by the model include the correct nM or pM units and compare them against the original documents.
- Query specific antibody sequences. Check if the recall results precisely point to document fragments containing that sequence and evaluate if the number of recalled documents is reasonable.
- Simulate user questions about newly published clinical trial reports. Observe if the model can promptly integrate and cite the latest data.
The values provided are common starting points. Measure performance against your own data 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.