Drug Discovery Agents¶
Configuration File: drug_discovery_agents.json
Tool Type: Local
Tools Count: 7
This page contains all tools defined in the drug_discovery_agents.json
configuration file.
Available Tools¶
ADMETAnalyzerAgent (Type: AgenticTool)¶
AI agent that analyzes ADMET data and provides insights on drug-likeness and safety profiles
ADMETAnalyzerAgent tool specification
Tool Information:
Name:
ADMETAnalyzerAgent
Type:
AgenticTool
Description: AI agent that analyzes ADMET data and provides insights on drug-likeness and safety profiles
Parameters:
compounds
(string) (required) List of compounds to analyze (comma-separated)admet_data
(string) (required) ADMET data from computational tools to analyzedisease_context
(string) (optional) Disease context for ADMET evaluation
Example Usage:
query = {
"name": "ADMETAnalyzerAgent",
"arguments": {
"compounds": "example_value",
"admet_data": "example_value"
}
}
result = tu.run(query)
ClinicalTrialDesignAgent (Type: AgenticTool)¶
AI agent that designs clinical trial protocols based on preclinical data and regulatory requirements
ClinicalTrialDesignAgent tool specification
Tool Information:
Name:
ClinicalTrialDesignAgent
Type:
AgenticTool
Description: AI agent that designs clinical trial protocols based on preclinical data and regulatory requirements
Parameters:
drug_name
(string) (required) Name of the drug candidateindication
(string) (required) Disease indicationpreclinical_data
(string) (optional) Preclinical efficacy and safety datatarget_population
(string) (optional) Target patient population
Example Usage:
query = {
"name": "ClinicalTrialDesignAgent",
"arguments": {
"drug_name": "example_value",
"indication": "example_value"
}
}
result = tu.run(query)
CompoundDiscoveryAgent (Type: AgenticTool)¶
AI agent that analyzes potential drug compounds using multiple strategies and LLM reasoning
CompoundDiscoveryAgent tool specification
Tool Information:
Name:
CompoundDiscoveryAgent
Type:
AgenticTool
Description: AI agent that analyzes potential drug compounds using multiple strategies and LLM reasoning
Parameters:
disease_name
(string) (required) Name of the diseasetargets
(string) (required) List of therapeutic targets (comma-separated)context
(string) (optional) Additional context or specific requirements
Example Usage:
query = {
"name": "CompoundDiscoveryAgent",
"arguments": {
"disease_name": "example_value",
"targets": "example_value"
}
}
result = tu.run(query)
DiseaseAnalyzerAgent (Type: AgenticTool)¶
AI agent that analyzes disease characteristics and identifies potential therapeutic targets using…
DiseaseAnalyzerAgent tool specification
Tool Information:
Name:
DiseaseAnalyzerAgent
Type:
AgenticTool
Description: AI agent that analyzes disease characteristics and identifies potential therapeutic targets using LLM reasoning
Parameters:
disease_name
(string) (required) Name of the disease to analyzecontext
(string) (optional) Additional context or specific focus areas
Example Usage:
query = {
"name": "DiseaseAnalyzerAgent",
"arguments": {
"disease_name": "example_value"
}
}
result = tu.run(query)
DrugInteractionAnalyzerAgent (Type: AgenticTool)¶
AI agent that analyzes drug-drug interactions and provides clinical recommendations
DrugInteractionAnalyzerAgent tool specification
Tool Information:
Name:
DrugInteractionAnalyzerAgent
Type:
AgenticTool
Description: AI agent that analyzes drug-drug interactions and provides clinical recommendations
Parameters:
compounds
(string) (required) List of compounds to analyze for interactions (comma-separated)patient_context
(string) (optional) Patient context (age, comorbidities, medications, etc.)
Example Usage:
query = {
"name": "DrugInteractionAnalyzerAgent",
"arguments": {
"compounds": "example_value"
}
}
result = tu.run(query)
DrugOptimizationAgent (Type: AgenticTool)¶
AI agent that analyzes drug optimization strategies based on ADMET and efficacy data
DrugOptimizationAgent tool specification
Tool Information:
Name:
DrugOptimizationAgent
Type:
AgenticTool
Description: AI agent that analyzes drug optimization strategies based on ADMET and efficacy data
Parameters:
compounds
(string) (required) List of compounds to optimize (comma-separated)admet_data
(string) (optional) ADMET properties and issuesefficacy_data
(string) (optional) Efficacy and potency datatarget_profile
(string) (optional) Target profile and requirements
Example Usage:
query = {
"name": "DrugOptimizationAgent",
"arguments": {
"compounds": "example_value"
}
}
result = tu.run(query)
LiteratureSynthesisAgent (Type: AgenticTool)¶
AI agent that synthesizes literature findings and provides evidence-based insights
LiteratureSynthesisAgent tool specification
Tool Information:
Name:
LiteratureSynthesisAgent
Type:
AgenticTool
Description: AI agent that synthesizes literature findings and provides evidence-based insights
Parameters:
topic
(string) (required) Research topic or questionliterature_data
(string) (required) Literature findings or abstracts to synthesizefocus_area
(string) (optional) Specific focus area for synthesis
Example Usage:
query = {
"name": "LiteratureSynthesisAgent",
"arguments": {
"topic": "example_value",
"literature_data": "example_value"
}
}
result = tu.run(query)