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https://github.com/RYDE-WORK/lnp_ml.git
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fix: 收窄优化搜索域，新增 /predict 端点
This commit is contained in:
Michelle0574
parent
aeddf8e1ee
commit
48b22cfe86
170
app/api.py
170
app/api.py
@ -25,23 +25,28 @@ from app.optimize import (
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TARGET_BIODIST,
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CompRanges,
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ScoringWeights,
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HELPER_LIPID_OPTIONS,
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ROUTE_OPTIONS,
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create_dataframe_from_formulations,
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predict_all,
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)
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# ============ Pydantic Models ============
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class CompRangesRequest(BaseModel):
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"""组分范围配置（mol 比例为百分数 0-100）"""
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weight_ratio_min: float = Field(default=5.0, ge=1.0, le=50.0, description="阳离子脂质/mRNA 重量比最小值")
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weight_ratio_max: float = Field(default=30.0, ge=1.0, le=50.0, description="阳离子脂质/mRNA 重量比最大值")
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cationic_mol_min: float = Field(default=5.0, ge=0.0, le=100.0, description="阳离子脂质 mol 比例最小值 (%)")
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cationic_mol_max: float = Field(default=80.0, ge=0.0, le=100.0, description="阳离子脂质 mol 比例最大值 (%)")
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phospholipid_mol_min: float = Field(default=0.0, ge=0.0, le=100.0, description="磷脂 mol 比例最小值 (%)")
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phospholipid_mol_max: float = Field(default=80.0, ge=0.0, le=100.0, description="磷脂 mol 比例最大值 (%)")
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cholesterol_mol_min: float = Field(default=0.0, ge=0.0, le=100.0, description="胆固醇 mol 比例最小值 (%)")
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cholesterol_mol_max: float = Field(default=80.0, ge=0.0, le=100.0, description="胆固醇 mol 比例最大值 (%)")
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peg_mol_min: float = Field(default=0.0, ge=0.0, le=20.0, description="PEG 脂质 mol 比例最小值 (%)")
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peg_mol_max: float = Field(default=5.0, ge=0.0, le=20.0, description="PEG 脂质 mol 比例最大值 (%)")
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"""组分范围配置（mol 比例为百分数 0-100）
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"""
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weight_ratio_min: float = Field(default=7.0, ge=1.0, le=50.0, description="阳离子脂质/mRNA 重量比最小值")
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weight_ratio_max: float = Field(default=20.0, ge=1.0, le=50.0, description="阳离子脂质/mRNA 重量比最大值")
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cationic_mol_min: float = Field(default=22.0, ge=0.0, le=100.0, description="阳离子脂质 mol 比例最小值 (%)")
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cationic_mol_max: float = Field(default=55.0, ge=0.0, le=100.0, description="阳离子脂质 mol 比例最大值 (%)")
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phospholipid_mol_min: float = Field(default=7.0, ge=0.0, le=100.0, description="磷脂 mol 比例最小值 (%)")
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phospholipid_mol_max: float = Field(default=42.0, ge=0.0, le=100.0, description="磷脂 mol 比例最大值 (%)")
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cholesterol_mol_min: float = Field(default=15.0, ge=0.0, le=100.0, description="胆固醇 mol 比例最小值 (%)")
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cholesterol_mol_max: float = Field(default=46.0, ge=0.0, le=100.0, description="胆固醇 mol 比例最大值 (%)")
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peg_mol_min: float = Field(default=1.0, ge=0.0, le=20.0, description="PEG 脂质 mol 比例最小值 (%)")
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peg_mol_max: float = Field(default=6.0, ge=0.0, le=20.0, description="PEG 脂质 mol 比例最大值 (%)")
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def to_comp_ranges(self) -> CompRanges:
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"""转换为 CompRanges 对象"""
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@ -151,6 +156,70 @@ class HealthResponse(BaseModel):
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use_rag: bool = False
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class PredictRequest(BaseModel):
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"""单配方预测请求"""
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smiles: str = Field(..., description="Cationic lipid SMILES")
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cationic_lipid_to_mrna_ratio: float = Field(..., gt=0, le=50, description="阳离子脂质/mRNA 重量比")
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cationic_lipid_mol_ratio: float = Field(..., ge=0, le=100, description="阳离子脂质 mol 比例 (%)")
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phospholipid_mol_ratio: float = Field(..., ge=0, le=100, description="磷脂 mol 比例 (%)")
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cholesterol_mol_ratio: float = Field(..., ge=0, le=100, description="胆固醇 mol 比例 (%)")
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peg_lipid_mol_ratio: float = Field(..., ge=0, le=20, description="PEG 脂质 mol 比例 (%)")
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helper_lipid: str = Field(default="DOPE", description=f"辅助脂质，可选 {HELPER_LIPID_OPTIONS}")
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route: str = Field(default="intravenous", description=f"给药途径，可选 {ROUTE_OPTIONS}")
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class Config:
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json_schema_extra = {
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"example": {
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"smiles": "CC(C)NCCNC(C)C",
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"cationic_lipid_to_mrna_ratio": 10.0,
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"cationic_lipid_mol_ratio": 50.0,
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"phospholipid_mol_ratio": 10.0,
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"cholesterol_mol_ratio": 38.5,
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"peg_lipid_mol_ratio": 1.5,
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"helper_lipid": "DOPE",
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"route": "intravenous",
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}
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}
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class PredictResponse(BaseModel):
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"""单配方预测响应"""
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smiles: str
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helper_lipid: str
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route: str
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biodist: Dict[str, float]
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size: Optional[float] = None
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quantified_delivery: Optional[float] = None
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unnormalized_delivery: Optional[float] = None
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pdi_class: Optional[int] = None
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ee_class: Optional[int] = None
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toxic_class: Optional[int] = None
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class Config:
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json_schema_extra = {
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"example": {
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"smiles": "CC(C)NCCNC(C)C",
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"helper_lipid": "DOPE",
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"route": "intravenous",
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"biodist": {
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"lymph_nodes": 0.0048,
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"heart": 0.0044,
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"liver": 0.6591,
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"spleen": 0.2817,
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"lung": 0.0245,
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"kidney": 0.0052,
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"muscle": 0.0203,
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},
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"size": 100.7,
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"quantified_delivery": 0.1169,
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"unnormalized_delivery": 0.3432,
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"pdi_class": 0,
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"ee_class": 2,
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"toxic_class": 0,
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}
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}
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# ============ Global State ============
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class ModelState:
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@ -261,6 +330,85 @@ async def get_available_organs():
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return AVAILABLE_ORGANS
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@app.post("/predict", response_model=PredictResponse)
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async def predict_formulation(request: PredictRequest):
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"""对单个指定配方做属性预测（不搜索，约 0.2 秒）。"""
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import pandas as pd
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from rdkit import Chem, RDLogger
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if state.model is None:
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raise HTTPException(status_code=503, detail="Model not loaded")
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if request.helper_lipid not in HELPER_LIPID_OPTIONS:
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raise HTTPException(
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status_code=400,
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detail=f"Invalid helper_lipid: {request.helper_lipid}. Available: {HELPER_LIPID_OPTIONS}",
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)
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if request.route not in ROUTE_OPTIONS:
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raise HTTPException(
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status_code=400,
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detail=f"Invalid route: {request.route}. Available: {ROUTE_OPTIONS}",
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)
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mol_sum = (
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request.cationic_lipid_mol_ratio
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+ request.phospholipid_mol_ratio
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+ request.cholesterol_mol_ratio
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+ request.peg_lipid_mol_ratio
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)
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if abs(mol_sum - 100.0) > 0.5:
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raise HTTPException(
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status_code=400, detail=f"四项 mol 比例之和须为 100（当前 {mol_sum:.2f}）"
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)
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RDLogger.DisableLog("rdApp.*")
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if Chem.MolFromSmiles(request.smiles) is None:
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raise HTTPException(status_code=400, detail=f"无法解析的 SMILES: {request.smiles[:80]}")
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logger.info(f"Predict request: helper={request.helper_lipid}, route={request.route}, "
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f"smiles={request.smiles[:50]}...")
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df = create_dataframe_from_formulations(
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request.smiles,
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[(
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request.cationic_lipid_to_mrna_ratio,
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request.cationic_lipid_mol_ratio,
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request.phospholipid_mol_ratio,
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request.cholesterol_mol_ratio,
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request.peg_lipid_mol_ratio,
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)],
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[request.helper_lipid],
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[request.route],
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)
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try:
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if getattr(state.model, "llm_prompt", None) is not None:
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state.model.set_llm_enabled(True)
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df = predict_all(state.model, df, state.device, batch_size=1)
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except Exception as e:
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logger.error(f"Prediction failed: {e}")
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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raise HTTPException(status_code=500, detail=str(e))
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row = df.iloc[0]
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_size, _unnorm = row.get("pred_size"), row.get("pred_unnorm_delivery")
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return PredictResponse(
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smiles=request.smiles,
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helper_lipid=request.helper_lipid,
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route=request.route,
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biodist={
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c.replace("Biodistribution_", ""): float(row[f"pred_{c}"]) for c in TARGET_BIODIST
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},
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size=float(_size) if pd.notna(_size) else None,
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quantified_delivery=float(row["pred_delivery"]),
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unnormalized_delivery=float(_unnorm) if pd.notna(_unnorm) else None,
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pdi_class=int(row["pred_pdi_class"]),
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ee_class=int(row["pred_ee_class"]),
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toxic_class=int(row["pred_toxic_class"]),
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)
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@app.post("/optimize", response_model=OptimizeResponse)
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async def optimize_formulation(request: OptimizeRequest):
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"""
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20
app/app.py
20
app/app.py
@ -460,37 +460,37 @@ def main():
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st.caption("阳离子脂质/mRNA 重量比")
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col1, col2 = st.columns(2)
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with col1:
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weight_ratio_min = st.number_input("最小", min_value=1.0, max_value=50.0, value=5.0, step=1.0, format="%.1f", key="wr_min")
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weight_ratio_min = st.number_input("最小", min_value=1.0, max_value=50.0, value=7.0, step=1.0, format="%.1f", key="wr_min")
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with col2:
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weight_ratio_max = st.number_input("最大", min_value=1.0, max_value=50.0, value=30.0, step=1.0, format="%.1f", key="wr_max")
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weight_ratio_max = st.number_input("最大", min_value=1.0, max_value=50.0, value=20.0, step=1.0, format="%.1f", key="wr_max")
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st.caption("阳离子脂质 mol 比例 (%)")
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col1, col2 = st.columns(2)
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with col1:
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cationic_mol_min = st.number_input("最小", min_value=0.0, max_value=100.0, value=5.0, step=5.0, format="%.1f", key="cat_min")
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cationic_mol_min = st.number_input("最小", min_value=0.0, max_value=100.0, value=22.0, step=5.0, format="%.1f", key="cat_min")
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with col2:
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cationic_mol_max = st.number_input("最大", min_value=0.0, max_value=100.0, value=80.0, step=5.0, format="%.1f", key="cat_max")
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cationic_mol_max = st.number_input("最大", min_value=0.0, max_value=100.0, value=55.0, step=5.0, format="%.1f", key="cat_max")
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st.caption("磷脂 mol 比例 (%)")
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col1, col2 = st.columns(2)
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with col1:
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phospholipid_mol_min = st.number_input("最小", min_value=0.0, max_value=100.0, value=0.0, step=5.0, format="%.1f", key="phos_min")
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phospholipid_mol_min = st.number_input("最小", min_value=0.0, max_value=100.0, value=7.0, step=5.0, format="%.1f", key="phos_min")
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with col2:
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phospholipid_mol_max = st.number_input("最大", min_value=0.0, max_value=100.0, value=80.0, step=5.0, format="%.1f", key="phos_max")
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phospholipid_mol_max = st.number_input("最大", min_value=0.0, max_value=100.0, value=42.0, step=5.0, format="%.1f", key="phos_max")
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st.caption("胆固醇 mol 比例 (%)")
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col1, col2 = st.columns(2)
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with col1:
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cholesterol_mol_min = st.number_input("最小", min_value=0.0, max_value=100.0, value=0.0, step=5.0, format="%.1f", key="chol_min")
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cholesterol_mol_min = st.number_input("最小", min_value=0.0, max_value=100.0, value=15.0, step=5.0, format="%.1f", key="chol_min")
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with col2:
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cholesterol_mol_max = st.number_input("最大", min_value=0.0, max_value=100.0, value=80.0, step=5.0, format="%.1f", key="chol_max")
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cholesterol_mol_max = st.number_input("最大", min_value=0.0, max_value=100.0, value=46.0, step=5.0, format="%.1f", key="chol_max")
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st.caption("PEG 脂质 mol 比例 (%)")
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col1, col2 = st.columns(2)
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with col1:
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peg_mol_min = st.number_input("最小", min_value=0.0, max_value=20.0, value=0.0, step=1.0, format="%.1f", key="peg_min")
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peg_mol_min = st.number_input("最小", min_value=0.0, max_value=20.0, value=1.0, step=1.0, format="%.1f", key="peg_min")
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with col2:
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peg_mol_max = st.number_input("最大", min_value=0.0, max_value=20.0, value=5.0, step=1.0, format="%.1f", key="peg_max")
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peg_mol_max = st.number_input("最大", min_value=0.0, max_value=20.0, value=6.0, step=1.0, format="%.1f", key="peg_max")
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comp_ranges = {
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"weight_ratio_min": weight_ratio_min,
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@ -45,22 +45,23 @@ AVAILABLE_ORGANS = ["lymph_nodes", "heart", "liver", "spleen", "lung", "kidney",
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@dataclass
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class CompRanges:
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"""组分参数范围配置（mol 比例为百分数 0-100）"""
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# 阳离子脂质/mRNA 重量比
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weight_ratio_min: float = 5.0
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weight_ratio_max: float = 30.0
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# 阳离子脂质 mol 比例 (%)
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cationic_mol_min: float = 5.0
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cationic_mol_max: float = 80.0
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# 磷脂 mol 比例 (%)
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phospholipid_mol_min: float = 0.0
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phospholipid_mol_max: float = 80.0
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# 胆固醇 mol 比例 (%)
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cholesterol_mol_min: float = 0.0
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cholesterol_mol_max: float = 80.0
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# PEG 脂质 mol 比例 (%)
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peg_mol_min: float = 0.0
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peg_mol_max: float = 5.0
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"""组分参数范围配置（mol 比例为百分数 0-100）
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"""
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# 阳离子脂质/mRNA 重量比（数据 1%–99%: 7.52–19.35）
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weight_ratio_min: float = 7.0
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weight_ratio_max: float = 20.0
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# 阳离子脂质 mol 比例 (%)（数据 1%–99%: 22.38–52.83）
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cationic_mol_min: float = 22.0
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cationic_mol_max: float = 55.0
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# 磷脂 mol 比例 (%)（数据 1%–99%: 7.50–40.50）
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phospholipid_mol_min: float = 7.0
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phospholipid_mol_max: float = 42.0
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# 胆固醇 mol 比例 (%)（数据 1%–99%: 16.00–45.00）
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cholesterol_mol_min: float = 15.0
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cholesterol_mol_max: float = 46.0
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# PEG 脂质 mol 比例 (%)（数据 1%–99%: 1.0–6.0）
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peg_mol_min: float = 1.0
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peg_mol_max: float = 6.0
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def to_dict(self) -> Dict:
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"""转换为字典"""
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