Tool Calling and Plugins for Lead Compound Screening in

Data Characteristics for This Category

Data for lead compound screening in pharmacovigilance primarily originates from high-throughput screening (HTS) experimental reports, computer-aided drug design (CADD) simulation results, in vitro pharmacological activity test data, and preliminary toxicology prediction reports. Data updates typically align with experimental batches and model iteration cycles, occurring weekly or monthly. Document structures vary, including structured data tables (e.g., CSV, Excel), semi-structured text reports (e.g., PDF, Word), and unstructured experimental logs. Fields and units are highly specialized. Examples include compound SMILES strings, CAS registry numbers, activity inhibition constants IC50 or Ki (units typically nM or µM), toxicity prediction indicators LD50 (unit mg/kg), ADMET (absorption, distribution, metabolism, excretion, toxicity) property prediction values, and various experimental condition parameters.

Constraints Imposed by These Characteristics on "Tool Calling and Plugins"

The diversity of lead compound screening data places multiple demands on tool calling and plugins. Structured data requires precise field mapping and data type conversion for seamless integration with external databases or analytical tools. Semi-structured and unstructured reports require plugins to have document parsing capabilities. Plugins must extract key compound information, activity data, and toxicology alerts from complex text. For example, if a report mentions "Compound X exhibits 80% CYP450 enzyme inhibition at a concentration of 100 nM," the plugin must accurately identify the compound name, concentration value, unit, and inhibition percentage. The update frequency dictates the data synchronization strategy, requiring scheduled tasks or event-triggered mechanisms. Specialized fields and units require plugins to understand and process domain-specific data formats. Examples include converting µM to nM for unified comparison or recognizing SMILES strings to call molecular structure analysis tools.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`maxContext`	`6000 characters`	Lead compound reports often contain extensive descriptive text, requiring sufficient context for comprehension.
`pluginTimeoutSeconds`	`120 seconds`	Tool calls for molecular structure analysis and ADMET prediction can be time-consuming.
`extractionPattern`	`Regular expression definition`	For precise matching of compound names, activity values, and toxicity indicators in different experimental reports.
`apiEndpoint`	`Specific tool API address`	To call external molecular databases or toxicology prediction services, for example, `https://chembl.api.com/lookup`.
`maxRetries`	`3 times`	External tool calls may fail due to network fluctuations or temporary service unavailability.
`dataConversionRules`	`JSON format rules`	To define unit conversions (e.g., `µM` to `nM`) and data type mappings.

Three Common Mistakes

Plugin call fails, logs show HTTP 400 Bad Request. This occurs because the compound SMILES string passed to the external tool has an incorrect format, or a critical parameter (e.g., IC50 value) is outside the tool's valid range.
The result returned by the tool call does not match expectations, for example, certain toxicity prediction indicators are missing. This often happens because the extractionPattern configured for the plugin is not comprehensive enough. It fails to cover all possible report text formats, preventing some key information from being successfully parsed and passed.
During debugging, the model's response contains extra numbers or content not present in the original answer. This may occur because the data returned by the tool is not strictly cleaned and validated before being processed by the model. For example, _id or timestamp fields in the raw JSON data returned by the tool are mistakenly interpreted as valid information and referenced by the model.

How to Confirm Correct Configuration

Select a test report containing typical lead compound screening data. Run a simulated conversation through the FastGPT platform. Observe whether the plugin accurately identifies and calls the corresponding external tools.
Check the tool call logs. Confirm that all configured apiEndpoints are accessed correctly and return a 200 OK status code, with no pluginTimeoutSeconds timeout errors.
Compare the raw data returned by the tool with the data processed by the plugin. Verify that dataConversionRules have been applied correctly. Ensure that field values and units meet expectations.
Test with a set of compounds with known toxicity or activity. Validate the pharmacovigilance recommendations provided by the model based on the tool's results. Determine if their accuracy and relevance reach an acceptable threshold.

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.