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Data Models for FastAPI Requests and Responses
"""
from pydantic import BaseModel, Field
from typing import List, Optional
from enum import Enum
class DiseaseSearchRequest(BaseModel):
"""Request model for disease target search"""
disease_name: str = Field(..., description="Name of the disease to search for")
top_n: int = Field(10, ge=1, le=100, description="Number of top targets to retrieve")
class Config:
example = {"disease_name": "Type 2 Diabetes", "top_n": 10}
class TargetInfo(BaseModel):
"""Protein target information"""
symbol: str
name: Optional[str] = None
score: float
sequence: Optional[str] = None
uniprot_id: str = ""
pdb_ids: List[str] = []
class Config:
example = {
"symbol": "INSR",
"name": "Insulin Receptor",
"score": 0.85,
"sequence": None,
"uniprot_id": "P06213",
"pdb_ids": ["2HR7", "3EKN", "4IBM"]
}
class DrugInfo(BaseModel):
"""Drug information"""
name: str
smiles: str
drug_id: Optional[str] = None
class Config:
example = {
"name": "Drug_001",
"smiles": "CC(=O)Oc1ccccc1C(=O)O",
"drug_id": "1"
}
class DrugCandidate(BaseModel):
"""Drug-target prediction candidate"""
drug_name: str
smiles: str = ""
target_symbol: str
uniprot_id: str = ""
binding_score: float
rank: int = 0
status: Optional[str] = None
class Config:
example = {
"drug_name": "Drug_001",
"smiles": "CC(=O)Oc1ccccc1C(=O)O",
"target_symbol": "INSR",
"uniprot_id": "P06213",
"binding_score": 0.85,
"rank": 1,
"status": "🆕 Potential Discovery"
}
class ScreeningRequest(BaseModel):
"""Request model for virtual drug screening"""
disease_name: str = Field(..., description="Disease name for target identification")
min_score: float = Field(0.0, ge=0.0, le=1.0, description="Minimum binding affinity score")
top_n_targets: int = Field(10, ge=1, le=50, description="Number of targets to use")
known_drugs: List[str] = Field(
default=["Metformin"],
description="List of known drugs for the disease (for filtering)"
)
class Config:
example = {
"disease_name": "Type 2 Diabetes",
"min_score": 0.5,
"top_n_targets": 10,
"known_drugs": ["Metformin", "Insulin"]
}
class ScreeningResponse(BaseModel):
"""Response model for screening results"""
disease_name: str
total_targets_found: int
total_drugs_screened: int
total_pairs_evaluated: int
top_candidates: List[DrugCandidate]
warnings: List[str] = []
class Config:
example = {
"disease_name": "Type 2 Diabetes",
"total_targets_found": 10,
"total_drugs_screened": 200,
"total_pairs_evaluated": 2000,
"top_candidates": [
{
"drug_name": "Drug_001",
"smiles": "CC(=O)Oc1ccccc1C(=O)O",
"target_symbol": "INSR",
"uniprot_id": "P06213",
"binding_score": 0.85,
"rank": 1,
"status": "🆕 Potential Discovery"
}
],
"warnings": []
}
class HealthCheckResponse(BaseModel):
"""Health check response"""
status: str
version: str
service: str
class Config:
example = {
"status": "healthy",
"version": "1.0.0",
"service": "Drug Repurposing API"
}
class EnrichedTargetResponse(BaseModel):
"""Response model for enriched targets endpoint"""
disease: str
disease_id: str
total_targets: int
targets: List[TargetInfo]
class Config:
example = {
"disease": "Type 2 Diabetes",
"disease_id": "EFO_0001360",
"total_targets": 10,
"targets": [
{
"symbol": "INSR",
"name": "Insulin Receptor",
"score": 0.85,
"sequence": None,
"uniprot_id": "P06213",
"pdb_ids": ["2HR7", "3EKN", "4IBM"]
}
]
}
class ErrorResponse(BaseModel):
"""Error response model"""
detail: str
error_code: Optional[str] = None
class Config:
example = {
"detail": "Disease not found",
"error_code": "DISEASE_NOT_FOUND"
}
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