Tool Calling and Plugins for Target Discovery Products

Data Characteristics in this Category

Target discovery data often originates from multiple sources. These include public genomics and proteomics databases (e.g., NCBI Gene, UniProt), disease association databases (e.g., OMIM, DisGeNET), compound libraries (e.g., PubChem, ChEMBL), and patent literature. Data update frequencies vary. Basic gene sequence and protein structure data updates are relatively stable, while high-throughput sequencing or preclinical research data may update periodically in batches. Document structures are diverse, including structured tabular data (e.g., gene-disease association tables), semi-structured text (e.g., literature abstracts, patent descriptions), and unstructured images (e.g., tissue sections, gel electrophoresis images). Common fields and units include gene IDs (Entrez ID, Ensembl ID), protein IDs (UniProt Accession), disease ontology IDs (DOID), compound SMILES strings, IC50 values (units nM or µM), affinity constants (Kd, unit nM), and expression data (e.g., FPKM, TPM).

Constraints on Tool Calling and Plugins from these Characteristics

The multi-source and heterogeneous nature of target discovery data requires tool calls to flexibly handle various input formats and structures, for example, by defining complex parameters via JSON Schema. Varying update frequencies mean caching strategies must be refined; static data can have longer expiration times, while dynamic research progress requires frequent synchronization. The presence of a large amount of unstructured text demands high natural language processing capabilities from plugins, requiring accurate extraction of key entities (e.g., gene names, disease names) and relationships from literature. Numerical fields like IC50 and Kd values require tool calls to recognize and handle unit conversions during parameter validation, preventing calculation errors due to unit mismatches. Additionally, complex chemical structure diagrams in patent literature may require image recognition plugins for preprocessing to convert them into structured data suitable for retrieval or analysis.

Configuration Settings

Configuration Item	Recommended Value	Rationale for this Value
`maxContext`	`3000 Tokens`	Target discovery literature abstracts and experimental reports are often long, requiring sufficient context for understanding.
`Chunk size` (Segment Length)	`500 characters` (characters)	Balances semantic completeness with recall precision, preventing long texts from diluting key information.
`Recall count` (Recall Count)	`10 entries` (items)	Considers both information volume and processing efficiency, ensuring coverage of sufficient potential target leads.
`Similarity threshold` (Similarity Threshold)	`0.75`	Improves recall accuracy for specialized terminology in biomedical texts.
`PARSE_FILE_TIMEOUT_SECONDS`	`120 seconds` (seconds)	Allows ample parsing time when processing large genomic datasets or multi-page patent documents.
`model`	`deepseek-r1`	This model performs well in understanding complex biomedical concepts and multi-step reasoning.

Three Common Pitfalls

Tool call returns 500 Internal Server Error: This usually occurs when the plugin's internal script fails to correctly parse complex data structures specific to target discovery, such as malformed compound SMILES strings, when processing input parameters.
AI response contains numerical calculation errors, such as inconsistent IC50 units: This happens because the tool call does not standardize or validate numerical parameters passed, leading to incorrect units used in backend calculations.
Retrieval of specific gene or disease information fails or yields empty results: This is often due to incorrect parameters in external knowledge base API calls, for example, a mismatch in gene ID types (Entrez ID instead of Ensembl ID), or an API Key that is not configured or expired.

How to Confirm Correct Configuration

Submit queries for different targets (e.g., GPCR, kinases) and diseases (e.g., cancer, autoimmune diseases). Check if the returned results include relevant gene, protein, pathway, and compound information.
Design inputs with various data types (text, numerical, structural formulas). Verify that tool calls correctly parse and pass parameters, for instance, IC50 values should be passed in nM units.
Simulate external knowledge base interface anomalies (e.g., network outage, API rate limiting). Check if FastGPT returns user-friendly error messages or executes predefined fallback strategies.

Note: 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.