From 48b22cfe861f9d001d70037b44b3d3d4bdd96214 Mon Sep 17 00:00:00 2001 From: Michelle0574 <2170308303@qq.com> Date: Sat, 15 Aug 2026 12:23:54 +0000 Subject: [PATCH] =?UTF-8?q?fix:=20=E6=94=B6=E7=AA=84=E4=BC=98=E5=8C=96?= =?UTF-8?q?=E6=90=9C=E7=B4=A2=E5=9F=9F=EF=BC=8C=E6=96=B0=E5=A2=9E=20/predi?= =?UTF-8?q?ct=20=E7=AB=AF=E7=82=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- app/api.py | 170 ++++++++++++++++++++++++++++++++++++++++++++---- app/app.py | 28 ++++---- app/optimize.py | 33 +++++----- 3 files changed, 190 insertions(+), 41 deletions(-) diff --git a/app/api.py b/app/api.py index 128f277..426f366 100644 --- a/app/api.py +++ b/app/api.py @@ -25,23 +25,28 @@ from app.optimize import ( TARGET_BIODIST, CompRanges, ScoringWeights, + HELPER_LIPID_OPTIONS, + ROUTE_OPTIONS, + create_dataframe_from_formulations, + predict_all, ) # ============ Pydantic Models ============ class CompRangesRequest(BaseModel): - """组分范围配置（mol 比例为百分数 0-100）""" - 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") - 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") - 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 (%)") - 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 (%)") - phospholipid_mol_min: float = Field(default=0.0, ge=0.0, le=100.0, description="磷脂 mol 比例最小值 (%)") - phospholipid_mol_max: float = Field(default=80.0, ge=0.0, le=100.0, description="磷脂 mol 比例最大值 (%)") - cholesterol_mol_min: float = Field(default=0.0, ge=0.0, le=100.0, description="胆固醇 mol 比例最小值 (%)") - cholesterol_mol_max: float = Field(default=80.0, ge=0.0, le=100.0, description="胆固醇 mol 比例最大值 (%)") - peg_mol_min: float = Field(default=0.0, ge=0.0, le=20.0, description="PEG 脂质 mol 比例最小值 (%)") - peg_mol_max: float = Field(default=5.0, ge=0.0, le=20.0, description="PEG 脂质 mol 比例最大值 (%)") + """组分范围配置（mol 比例为百分数 0-100） + """ + 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") + 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") + 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 (%)") + 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 (%)") + phospholipid_mol_min: float = Field(default=7.0, ge=0.0, le=100.0, description="磷脂 mol 比例最小值 (%)") + phospholipid_mol_max: float = Field(default=42.0, ge=0.0, le=100.0, description="磷脂 mol 比例最大值 (%)") + cholesterol_mol_min: float = Field(default=15.0, ge=0.0, le=100.0, description="胆固醇 mol 比例最小值 (%)") + cholesterol_mol_max: float = Field(default=46.0, ge=0.0, le=100.0, description="胆固醇 mol 比例最大值 (%)") + peg_mol_min: float = Field(default=1.0, ge=0.0, le=20.0, description="PEG 脂质 mol 比例最小值 (%)") + peg_mol_max: float = Field(default=6.0, ge=0.0, le=20.0, description="PEG 脂质 mol 比例最大值 (%)") def to_comp_ranges(self) -> CompRanges: """转换为 CompRanges 对象""" @@ -151,6 +156,70 @@ class HealthResponse(BaseModel): use_rag: bool = False +class PredictRequest(BaseModel): + """单配方预测请求""" + smiles: str = Field(..., description="Cationic lipid SMILES") + cationic_lipid_to_mrna_ratio: float = Field(..., gt=0, le=50, description="阳离子脂质/mRNA 重量比") + cationic_lipid_mol_ratio: float = Field(..., ge=0, le=100, description="阳离子脂质 mol 比例 (%)") + phospholipid_mol_ratio: float = Field(..., ge=0, le=100, description="磷脂 mol 比例 (%)") + cholesterol_mol_ratio: float = Field(..., ge=0, le=100, description="胆固醇 mol 比例 (%)") + peg_lipid_mol_ratio: float = Field(..., ge=0, le=20, description="PEG 脂质 mol 比例 (%)") + helper_lipid: str = Field(default="DOPE", description=f"辅助脂质，可选 {HELPER_LIPID_OPTIONS}") + route: str = Field(default="intravenous", description=f"给药途径，可选 {ROUTE_OPTIONS}") + + class Config: + json_schema_extra = { + "example": { + "smiles": "CC(C)NCCNC(C)C", + "cationic_lipid_to_mrna_ratio": 10.0, + "cationic_lipid_mol_ratio": 50.0, + "phospholipid_mol_ratio": 10.0, + "cholesterol_mol_ratio": 38.5, + "peg_lipid_mol_ratio": 1.5, + "helper_lipid": "DOPE", + "route": "intravenous", + } + } + + +class PredictResponse(BaseModel): + """单配方预测响应""" + smiles: str + helper_lipid: str + route: str + biodist: Dict[str, float] + size: Optional[float] = None + quantified_delivery: Optional[float] = None + unnormalized_delivery: Optional[float] = None + pdi_class: Optional[int] = None + ee_class: Optional[int] = None + toxic_class: Optional[int] = None + + class Config: + json_schema_extra = { + "example": { + "smiles": "CC(C)NCCNC(C)C", + "helper_lipid": "DOPE", + "route": "intravenous", + "biodist": { + "lymph_nodes": 0.0048, + "heart": 0.0044, + "liver": 0.6591, + "spleen": 0.2817, + "lung": 0.0245, + "kidney": 0.0052, + "muscle": 0.0203, + }, + "size": 100.7, + "quantified_delivery": 0.1169, + "unnormalized_delivery": 0.3432, + "pdi_class": 0, + "ee_class": 2, + "toxic_class": 0, + } + } + + # ============ Global State ============ class ModelState: @@ -261,6 +330,85 @@ async def get_available_organs(): return AVAILABLE_ORGANS +@app.post("/predict", response_model=PredictResponse) +async def predict_formulation(request: PredictRequest): + """对单个指定配方做属性预测（不搜索，约 0.2 秒）。""" + import pandas as pd + from rdkit import Chem, RDLogger + + if state.model is None: + raise HTTPException(status_code=503, detail="Model not loaded") + + if request.helper_lipid not in HELPER_LIPID_OPTIONS: + raise HTTPException( + status_code=400, + detail=f"Invalid helper_lipid: {request.helper_lipid}. Available: {HELPER_LIPID_OPTIONS}", + ) + if request.route not in ROUTE_OPTIONS: + raise HTTPException( + status_code=400, + detail=f"Invalid route: {request.route}. Available: {ROUTE_OPTIONS}", + ) + + mol_sum = ( + request.cationic_lipid_mol_ratio + + request.phospholipid_mol_ratio + + request.cholesterol_mol_ratio + + request.peg_lipid_mol_ratio + ) + if abs(mol_sum - 100.0) > 0.5: + raise HTTPException( + status_code=400, detail=f"四项 mol 比例之和须为 100（当前 {mol_sum:.2f}）" + ) + + RDLogger.DisableLog("rdApp.*") + if Chem.MolFromSmiles(request.smiles) is None: + raise HTTPException(status_code=400, detail=f"无法解析的 SMILES: {request.smiles[:80]}") + + logger.info(f"Predict request: helper={request.helper_lipid}, route={request.route}, " + f"smiles={request.smiles[:50]}...") + + df = create_dataframe_from_formulations( + request.smiles, + [( + request.cationic_lipid_to_mrna_ratio, + request.cationic_lipid_mol_ratio, + request.phospholipid_mol_ratio, + request.cholesterol_mol_ratio, + request.peg_lipid_mol_ratio, + )], + [request.helper_lipid], + [request.route], + ) + + try: + if getattr(state.model, "llm_prompt", None) is not None: + state.model.set_llm_enabled(True) + df = predict_all(state.model, df, state.device, batch_size=1) + except Exception as e: + logger.error(f"Prediction failed: {e}") + if torch.cuda.is_available(): + torch.cuda.empty_cache() + raise HTTPException(status_code=500, detail=str(e)) + + row = df.iloc[0] + _size, _unnorm = row.get("pred_size"), row.get("pred_unnorm_delivery") + return PredictResponse( + smiles=request.smiles, + helper_lipid=request.helper_lipid, + route=request.route, + biodist={ + c.replace("Biodistribution_", ""): float(row[f"pred_{c}"]) for c in TARGET_BIODIST + }, + size=float(_size) if pd.notna(_size) else None, + quantified_delivery=float(row["pred_delivery"]), + unnormalized_delivery=float(_unnorm) if pd.notna(_unnorm) else None, + pdi_class=int(row["pred_pdi_class"]), + ee_class=int(row["pred_ee_class"]), + toxic_class=int(row["pred_toxic_class"]), + ) + + @app.post("/optimize", response_model=OptimizeResponse) async def optimize_formulation(request: OptimizeRequest): """ diff --git a/app/app.py b/app/app.py index aa6e20f..b8583a5 100644 --- a/app/app.py +++ b/app/app.py @@ -460,37 +460,37 @@ def main(): st.caption("阳离子脂质/mRNA 重量比") col1, col2 = st.columns(2) with col1: - 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") + 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") with col2: - 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") - + 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") + st.caption("阳离子脂质 mol 比例 (%)") col1, col2 = st.columns(2) with col1: - 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") + 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") with col2: - 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") - + 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") + st.caption("磷脂 mol 比例 (%)") col1, col2 = st.columns(2) with col1: - 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") + 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") with col2: - 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") - + 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") + st.caption("胆固醇 mol 比例 (%)") col1, col2 = st.columns(2) with col1: - 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") + 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") with col2: - 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") - + 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") + st.caption("PEG 脂质 mol 比例 (%)") col1, col2 = st.columns(2) with col1: - 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") + 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") with col2: - 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") + 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") comp_ranges = { "weight_ratio_min": weight_ratio_min, diff --git a/app/optimize.py b/app/optimize.py index ba389ce..605f47a 100644 --- a/app/optimize.py +++ b/app/optimize.py @@ -45,22 +45,23 @@ AVAILABLE_ORGANS = ["lymph_nodes", "heart", "liver", "spleen", "lung", "kidney", @dataclass class CompRanges: - """组分参数范围配置（mol 比例为百分数 0-100）""" - # 阳离子脂质/mRNA 重量比 - weight_ratio_min: float = 5.0 - weight_ratio_max: float = 30.0 - # 阳离子脂质 mol 比例 (%) - cationic_mol_min: float = 5.0 - cationic_mol_max: float = 80.0 - # 磷脂 mol 比例 (%) - phospholipid_mol_min: float = 0.0 - phospholipid_mol_max: float = 80.0 - # 胆固醇 mol 比例 (%) - cholesterol_mol_min: float = 0.0 - cholesterol_mol_max: float = 80.0 - # PEG 脂质 mol 比例 (%) - peg_mol_min: float = 0.0 - peg_mol_max: float = 5.0 + """组分参数范围配置（mol 比例为百分数 0-100） + """ + # 阳离子脂质/mRNA 重量比（数据 1%–99%: 7.52–19.35） + weight_ratio_min: float = 7.0 + weight_ratio_max: float = 20.0 + # 阳离子脂质 mol 比例 (%)（数据 1%–99%: 22.38–52.83） + cationic_mol_min: float = 22.0 + cationic_mol_max: float = 55.0 + # 磷脂 mol 比例 (%)（数据 1%–99%: 7.50–40.50） + phospholipid_mol_min: float = 7.0 + phospholipid_mol_max: float = 42.0 + # 胆固醇 mol 比例 (%)（数据 1%–99%: 16.00–45.00） + cholesterol_mol_min: float = 15.0 + cholesterol_mol_max: float = 46.0 + # PEG 脂质 mol 比例 (%)（数据 1%–99%: 1.0–6.0） + peg_mol_min: float = 1.0 + peg_mol_max: float = 6.0 def to_dict(self) -> Dict: """转换为字典"""