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75e1dcb0eb
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93a6f8654d
40
Makefile
40
Makefile
@ -68,11 +68,6 @@ clean_data: requirements
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data: requirements
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$(PYTHON_INTERPRETER) scripts/process_data.py
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## Process dataset for final training (interim -> processed/final, train:val=9:1, no test)
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.PHONY: data_final
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data_final: requirements
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$(PYTHON_INTERPRETER) scripts/process_data_final.py
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## Process external data for pretrain (external -> processed)
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.PHONY: data_pretrain
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data_pretrain: requirements
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@ -133,16 +128,6 @@ train: requirements
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finetune: requirements
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$(PYTHON_INTERPRETER) -m lnp_ml.modeling.train --init-from-pretrain models/pretrain_delivery.pt $(FREEZE_FLAG) $(MPNN_FLAG) $(DEVICE_FLAG)
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## Final training using all data (train:val=9:1, no test set), with pretrained weights
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.PHONY: train_final
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train_final: requirements
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$(PYTHON_INTERPRETER) -m lnp_ml.modeling.train \
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--train-path data/processed/final/train.parquet \
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--val-path data/processed/final/val.parquet \
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--output-dir models/final \
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--init-from-pretrain models/pretrain_delivery.pt \
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$(FREEZE_FLAG) $(MPNN_FLAG) $(DEVICE_FLAG)
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## Finetune with cross-validation on internal data (5-fold, amine-based split) with pretrained weights
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.PHONY: finetune_cv
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finetune_cv: requirements
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@ -174,31 +159,6 @@ predict: requirements
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test: requirements
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$(PYTHON_INTERPRETER) -m lnp_ml.modeling.predict test $(DEVICE_FLAG)
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## Formulation optimization: find optimal LNP formulation for target organ
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## Usage: make optimize SMILES="CC(C)..." ORGAN=liver
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.PHONY: optimize
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optimize: requirements
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$(PYTHON_INTERPRETER) -m app.optimize --smiles "$(SMILES)" --organ $(ORGAN) $(DEVICE_FLAG)
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## Start FastAPI backend server (port 8000)
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.PHONY: api
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api: requirements
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uvicorn app.api:app --host 0.0.0.0 --port 8000 --reload
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## Start Streamlit frontend app (port 8501)
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.PHONY: webapp
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webapp: requirements
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streamlit run app/app.py --server.port 8501
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## Start both API and webapp (run in separate terminals)
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.PHONY: serve
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serve:
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@echo "请在两个终端分别运行:"
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@echo " 终端 1: make api"
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@echo " 终端 2: make webapp"
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@echo ""
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@echo "然后访问: http://localhost:8501"
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#################################################################################
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# Self Documenting Commands #
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@ -156,12 +156,5 @@ python -m lnp_ml.modeling.train \
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└── plots.py <- Code to create visualizations
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```
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### 配方筛选
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```
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make optimize SMILES="CC(C)NCCNC(C)C" ORGAN=liver
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```
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--------
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62
app/PARAM.md
62
app/PARAM.md
@ -1,62 +0,0 @@
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## Possible Values
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# comp token([B, 5], the sum of the latter four ratio is always 1)
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Cationic_Lipid_to_mRNA_weight_ratio(float, Min: 0.05, Max: 0.3, Step Size: 0.01)
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Cationic_Lipid_Mol_Ratio(float, Min: 0.05, Max: 0.8, Step Size: 0.01)
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Phospholipid_Mol_Ratio(float, Min: 0, Max: 0.8, Step Size: 0.01)
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Cholesterol_Mol_Ratio(float, Min: 0, Max: 0.8, Step Size: 0.01)
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PEG_Lipid_Mol_Ratio(float, Min: 0, Max: 0.05, Step Size: 0.01)
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# phys token([B, 12])
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Purity_Pure(one-hot for Purity, always Pure)
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Purity_Crude(one-hot for Purity, always Pure)
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Mix_type_Microfluidic(one-hot for Mix_type, always Microfluidic)
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Mix_type_Microfluidic(one-hot for Mix_type, always Microfluidic)
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Cargo_type_mRNA(one-hot for Cargo_type, always mRNA)
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Cargo_type_pDNA(one-hot for Cargo_type, always mRNA)
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Cargo_type_siRNA(one-hot for Cargo_type, always mRNA)
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Target_or_delivered_gene_FFL(one-hot for Target_or_delivered_gene, always FFL)
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Target_or_delivered_gene_Peptide_barcode(one-hot for Target_or_delivered_gene, always FFL)
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Target_or_delivered_gene_hEPO(one-hot for Target_or_delivered_gene, always FFL)
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Target_or_delivered_gene_FVII(one-hot for Target_or_delivered_gene, always FFL)
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Target_or_delivered_gene_GFP(one-hot for Target_or_delivered_gene, always FFL)
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# help token([B, 4])
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Helper_lipid_ID_DOPE(one-hot for Helper_lipid_ID, one of {DOPE, DSPC, DOTAP})
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Helper_lipid_ID_DOTAP(one-hot for Helper_lipid_ID, one of {DOPE, DSPC, DOTAP})
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Helper_lipid_ID_DSPC(one-hot for Helper_lipid_ID, one of {DOPE, DSPC, DOTAP})
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Helper_lipid_ID_MDOA(one-hot for Helper_lipid_ID, one of {DOPE, DSPC, DOTAP})
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# exp token([B, 32])
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Model_type_A549(one-hot for Model_type, always Mouse)
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Model_type_BDMC(one-hot for Model_type, always Mouse)
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Model_type_BMDM(one-hot for Model_type, always Mouse)
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Model_type_HBEC_ALI(one-hot for Model_type, always Mouse)
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Model_type_HEK293T(one-hot for Model_type, always Mouse)
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Model_type_HeLa(one-hot for Model_type, always Mouse)
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Model_type_IGROV1(one-hot for Model_type, always Mouse)
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Model_type_Mouse(one-hot for Model_type, always Mouse)
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Model_type_RAW264p7(one-hot for Model_type, always Mouse)
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Delivery_target_dendritic_cell(one-hot for Delivery_target, always body)
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Delivery_target_generic_cell(one-hot for Delivery_target, always body)
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Delivery_target_liver(one-hot for Delivery_target, always body)
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Delivery_target_lung(one-hot for Delivery_target, always body)
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Delivery_target_lung_epithelium(one-hot for Delivery_target, always body)
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Delivery_target_macrophage(one-hot for Delivery_target, always body)
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Delivery_target_muscle(one-hot for Delivery_target, always body)
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Delivery_target_spleen(one-hot for Delivery_target, always body)
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Delivery_target_body(one-hot for Delivery_target, always body)
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Route_of_administration_in_vitro(one-hot for Route_of_administration, one of {Intravenous, Intramuscular})
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Route_of_administration_intravenous(one-hot for Route_of_administration, one of {Intravenous, Intramuscular})
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Route_of_administration_intramuscular(one-hot for Route_of_administration, one of {Intravenous, Intramuscular})
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Route_of_administration_intratracheal(one-hot for Route_of_administration, one of {Intravenous, Intramuscular})
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Sample_organization_type_individual(one-hot for Sample_organization_type, always Individual)
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Sample_organization_type_barcoded(one-hot for Sample_organization_type, always Individual)
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Value_name_log_luminescence(one-hot for Value_name, always luminescence)
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Value_name_luminescence(one-hot for Value_name, always luminescence)
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Value_name_FFL_silencing(one-hot for Value_name, always luminescence)
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Value_name_Peptide_abundance(one-hot for Value_name, always luminescence)
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Value_name_hEPO(one-hot for Value_name, always luminescence)
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Value_name_FVII_silencing(one-hot for Value_name, always luminescence)
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Value_name_GFP_delivery(one-hot for Value_name, always luminescence)
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Value_name_Discretized_luminescence(one-hot for Value_name, always luminescence)
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@ -1,2 +0,0 @@
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"""LNP 配方优化应用"""
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244
app/api.py
244
app/api.py
@ -1,244 +0,0 @@
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"""
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FastAPI 配方优化 API
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启动服务:
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uvicorn app.api:app --host 0.0.0.0 --port 8000 --reload
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"""
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import os
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from pathlib import Path
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from typing import List, Dict, Optional
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from contextlib import asynccontextmanager
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import torch
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from fastapi import FastAPI, HTTPException
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from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel, Field
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from loguru import logger
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from lnp_ml.config import MODELS_DIR
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from lnp_ml.modeling.predict import load_model
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from app.optimize import (
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optimize,
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format_results,
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AVAILABLE_ORGANS,
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TARGET_BIODIST,
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)
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# ============ Pydantic Models ============
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class OptimizeRequest(BaseModel):
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"""优化请求"""
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smiles: str = Field(..., description="Cationic lipid SMILES string")
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organ: str = Field(..., description="Target organ for optimization")
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top_k: int = Field(default=20, ge=1, le=100, description="Number of top formulations")
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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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"organ": "liver",
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"top_k": 20
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}
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}
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||||
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||||
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class FormulationResult(BaseModel):
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"""单个配方结果"""
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rank: int
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target_biodist: float
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cationic_lipid_to_mrna_ratio: float
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cationic_lipid_mol_ratio: float
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phospholipid_mol_ratio: float
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cholesterol_mol_ratio: float
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peg_lipid_mol_ratio: float
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helper_lipid: str
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route: str
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||||
all_biodist: Dict[str, float]
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class OptimizeResponse(BaseModel):
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||||
"""优化响应"""
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||||
smiles: str
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target_organ: str
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formulations: List[FormulationResult]
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message: str
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||||
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||||
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||||
class HealthResponse(BaseModel):
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"""健康检查响应"""
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||||
status: str
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||||
model_loaded: bool
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||||
device: str
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||||
available_organs: List[str]
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||||
|
||||
|
||||
# ============ Global State ============
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||||
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||||
class ModelState:
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||||
"""模型状态管理"""
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||||
model = None
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||||
device = None
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||||
model_path = None
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||||
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||||
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||||
state = ModelState()
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||||
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||||
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||||
# ============ Lifespan ============
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||||
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||||
@asynccontextmanager
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||||
async def lifespan(app: FastAPI):
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"""应用生命周期管理：启动时加载模型"""
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||||
# Startup
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||||
logger.info("Starting API server...")
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||||
|
||||
# 确定设备
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||||
if torch.cuda.is_available():
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||||
device_str = "cuda"
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||||
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
|
||||
device_str = "mps"
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||||
else:
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||||
device_str = "cpu"
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||||
|
||||
# 可通过环境变量覆盖
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||||
device_str = os.environ.get("DEVICE", device_str)
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state.device = torch.device(device_str)
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logger.info(f"Using device: {state.device}")
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|
||||
# 加载模型
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||||
model_path = Path(os.environ.get("MODEL_PATH", MODELS_DIR / "final" / "model.pt"))
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state.model_path = model_path
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||||
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||||
logger.info(f"Loading model from {model_path}...")
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||||
try:
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||||
state.model = load_model(model_path, state.device)
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||||
logger.success("Model loaded successfully!")
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to load model: {e}")
|
||||
raise
|
||||
|
||||
yield
|
||||
|
||||
# Shutdown
|
||||
logger.info("Shutting down API server...")
|
||||
state.model = None
|
||||
torch.cuda.empty_cache() if torch.cuda.is_available() else None
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||||
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||||
|
||||
# ============ FastAPI App ============
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||||
|
||||
app = FastAPI(
|
||||
title="LNP 配方优化 API",
|
||||
description="基于深度学习的 LNP 纳米颗粒配方优化服务",
|
||||
version="1.0.0",
|
||||
lifespan=lifespan,
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||||
)
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||||
|
||||
# CORS 配置
|
||||
app.add_middleware(
|
||||
CORSMiddleware,
|
||||
allow_origins=["*"],
|
||||
allow_credentials=True,
|
||||
allow_methods=["*"],
|
||||
allow_headers=["*"],
|
||||
)
|
||||
|
||||
|
||||
# ============ Endpoints ============
|
||||
|
||||
@app.get("/", response_model=HealthResponse)
|
||||
async def health_check():
|
||||
"""健康检查"""
|
||||
return HealthResponse(
|
||||
status="healthy" if state.model is not None else "model_not_loaded",
|
||||
model_loaded=state.model is not None,
|
||||
device=str(state.device),
|
||||
available_organs=AVAILABLE_ORGANS,
|
||||
)
|
||||
|
||||
|
||||
@app.get("/organs", response_model=List[str])
|
||||
async def get_available_organs():
|
||||
"""获取可用的目标器官列表"""
|
||||
return AVAILABLE_ORGANS
|
||||
|
||||
|
||||
@app.post("/optimize", response_model=OptimizeResponse)
|
||||
async def optimize_formulation(request: OptimizeRequest):
|
||||
"""
|
||||
执行配方优化
|
||||
|
||||
通过迭代式 Grid Search 寻找最大化目标器官 Biodistribution 的最优配方。
|
||||
"""
|
||||
# 验证模型状态
|
||||
if state.model is None:
|
||||
raise HTTPException(status_code=503, detail="Model not loaded")
|
||||
|
||||
# 验证器官
|
||||
if request.organ not in AVAILABLE_ORGANS:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail=f"Invalid organ: {request.organ}. Available: {AVAILABLE_ORGANS}"
|
||||
)
|
||||
|
||||
# 验证 SMILES
|
||||
if not request.smiles or len(request.smiles.strip()) == 0:
|
||||
raise HTTPException(status_code=400, detail="SMILES string cannot be empty")
|
||||
|
||||
logger.info(f"Optimization request: organ={request.organ}, smiles={request.smiles[:50]}...")
|
||||
|
||||
try:
|
||||
# 执行优化
|
||||
results = optimize(
|
||||
smiles=request.smiles,
|
||||
organ=request.organ,
|
||||
model=state.model,
|
||||
device=state.device,
|
||||
top_k=request.top_k,
|
||||
batch_size=256,
|
||||
)
|
||||
|
||||
# 转换结果
|
||||
formulations = []
|
||||
for i, f in enumerate(results):
|
||||
formulations.append(FormulationResult(
|
||||
rank=i + 1,
|
||||
target_biodist=f.get_biodist(request.organ),
|
||||
cationic_lipid_to_mrna_ratio=f.cationic_lipid_to_mrna_ratio,
|
||||
cationic_lipid_mol_ratio=f.cationic_lipid_mol_ratio,
|
||||
phospholipid_mol_ratio=f.phospholipid_mol_ratio,
|
||||
cholesterol_mol_ratio=f.cholesterol_mol_ratio,
|
||||
peg_lipid_mol_ratio=f.peg_lipid_mol_ratio,
|
||||
helper_lipid=f.helper_lipid,
|
||||
route=f.route,
|
||||
all_biodist={
|
||||
col.replace("Biodistribution_", ""): f.biodist_predictions.get(col, 0.0)
|
||||
for col in TARGET_BIODIST
|
||||
},
|
||||
))
|
||||
|
||||
logger.success(f"Optimization completed: {len(formulations)} formulations")
|
||||
|
||||
return OptimizeResponse(
|
||||
smiles=request.smiles,
|
||||
target_organ=request.organ,
|
||||
formulations=formulations,
|
||||
message=f"Successfully found top {len(formulations)} formulations for {request.organ}",
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Optimization failed: {e}")
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import uvicorn
|
||||
uvicorn.run(
|
||||
"app.api:app",
|
||||
host="0.0.0.0",
|
||||
port=8000,
|
||||
reload=True,
|
||||
)
|
||||
|
||||
395
app/app.py
395
app/app.py
@ -1,395 +0,0 @@
|
||||
"""
|
||||
Streamlit 配方优化交互界面
|
||||
|
||||
启动应用:
|
||||
streamlit run app/app.py
|
||||
"""
|
||||
|
||||
import io
|
||||
from datetime import datetime
|
||||
|
||||
import httpx
|
||||
import pandas as pd
|
||||
import streamlit as st
|
||||
|
||||
# ============ 配置 ============
|
||||
|
||||
API_URL = "http://localhost:8000"
|
||||
|
||||
AVAILABLE_ORGANS = [
|
||||
"liver",
|
||||
"spleen",
|
||||
"lung",
|
||||
"heart",
|
||||
"kidney",
|
||||
"muscle",
|
||||
"lymph_nodes",
|
||||
]
|
||||
|
||||
ORGAN_LABELS = {
|
||||
"liver": "🫀 肝脏 (Liver)",
|
||||
"spleen": "🟣 脾脏 (Spleen)",
|
||||
"lung": "🫁 肺 (Lung)",
|
||||
"heart": "❤️ 心脏 (Heart)",
|
||||
"kidney": "🫘 肾脏 (Kidney)",
|
||||
"muscle": "💪 肌肉 (Muscle)",
|
||||
"lymph_nodes": "🔵 淋巴结 (Lymph Nodes)",
|
||||
}
|
||||
|
||||
# ============ 页面配置 ============
|
||||
|
||||
st.set_page_config(
|
||||
page_title="LNP 配方优化",
|
||||
page_icon="🧬",
|
||||
layout="wide",
|
||||
initial_sidebar_state="expanded",
|
||||
)
|
||||
|
||||
# ============ 自定义样式 ============
|
||||
|
||||
st.markdown("""
|
||||
<style>
|
||||
/* 主标题样式 */
|
||||
.main-title {
|
||||
font-size: 2.5rem;
|
||||
font-weight: 700;
|
||||
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
|
||||
-webkit-background-clip: text;
|
||||
-webkit-text-fill-color: transparent;
|
||||
text-align: center;
|
||||
margin-bottom: 0.5rem;
|
||||
}
|
||||
|
||||
/* 副标题样式 */
|
||||
.sub-title {
|
||||
font-size: 1.1rem;
|
||||
color: #6c757d;
|
||||
text-align: center;
|
||||
margin-bottom: 2rem;
|
||||
}
|
||||
|
||||
/* 结果卡片 */
|
||||
.result-card {
|
||||
background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
|
||||
border-radius: 12px;
|
||||
padding: 1.5rem;
|
||||
margin-bottom: 1rem;
|
||||
}
|
||||
|
||||
/* 指标高亮 */
|
||||
.metric-highlight {
|
||||
font-size: 2rem;
|
||||
font-weight: 700;
|
||||
color: #667eea;
|
||||
}
|
||||
|
||||
/* 侧边栏样式 */
|
||||
.sidebar-section {
|
||||
background: #f8f9fa;
|
||||
border-radius: 8px;
|
||||
padding: 1rem;
|
||||
margin-bottom: 1rem;
|
||||
}
|
||||
|
||||
/* 状态指示器 */
|
||||
.status-online {
|
||||
color: #28a745;
|
||||
font-weight: 600;
|
||||
}
|
||||
|
||||
.status-offline {
|
||||
color: #dc3545;
|
||||
font-weight: 600;
|
||||
}
|
||||
|
||||
/* 表格样式优化 */
|
||||
.dataframe {
|
||||
font-size: 0.85rem;
|
||||
}
|
||||
</style>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
|
||||
# ============ 辅助函数 ============
|
||||
|
||||
def check_api_status() -> bool:
|
||||
"""检查 API 状态"""
|
||||
try:
|
||||
with httpx.Client(timeout=5) as client:
|
||||
response = client.get(f"{API_URL}/")
|
||||
return response.status_code == 200
|
||||
except:
|
||||
return False
|
||||
|
||||
|
||||
def call_optimize_api(smiles: str, organ: str, top_k: int = 20) -> dict:
|
||||
"""调用优化 API"""
|
||||
with httpx.Client(timeout=300) as client: # 5 分钟超时
|
||||
response = client.post(
|
||||
f"{API_URL}/optimize",
|
||||
json={
|
||||
"smiles": smiles,
|
||||
"organ": organ,
|
||||
"top_k": top_k,
|
||||
},
|
||||
)
|
||||
response.raise_for_status()
|
||||
return response.json()
|
||||
|
||||
|
||||
def format_results_dataframe(results: dict) -> pd.DataFrame:
|
||||
"""将 API 结果转换为 DataFrame"""
|
||||
formulations = results["formulations"]
|
||||
target_organ = results["target_organ"]
|
||||
|
||||
rows = []
|
||||
for f in formulations:
|
||||
row = {
|
||||
"排名": f["rank"],
|
||||
f"Biodist_{target_organ}": f"{f['target_biodist']:.4f}",
|
||||
"阳离子脂质/mRNA": f["cationic_lipid_to_mrna_ratio"],
|
||||
"阳离子脂质(mol)": f["cationic_lipid_mol_ratio"],
|
||||
"磷脂(mol)": f["phospholipid_mol_ratio"],
|
||||
"胆固醇(mol)": f["cholesterol_mol_ratio"],
|
||||
"PEG脂质(mol)": f["peg_lipid_mol_ratio"],
|
||||
"辅助脂质": f["helper_lipid"],
|
||||
"给药途径": f["route"],
|
||||
}
|
||||
# 添加其他器官的 biodist
|
||||
for organ, value in f["all_biodist"].items():
|
||||
if organ != target_organ:
|
||||
row[f"Biodist_{organ}"] = f"{value:.4f}"
|
||||
rows.append(row)
|
||||
|
||||
return pd.DataFrame(rows)
|
||||
|
||||
|
||||
def create_export_csv(df: pd.DataFrame, smiles: str, organ: str) -> str:
|
||||
"""创建导出用的 CSV 内容"""
|
||||
# 添加元信息
|
||||
meta_info = f"# LNP 配方优化结果\n# SMILES: {smiles}\n# 目标器官: {organ}\n# 导出时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
|
||||
csv_content = df.to_csv(index=False)
|
||||
return meta_info + csv_content
|
||||
|
||||
|
||||
# ============ 主界面 ============
|
||||
|
||||
def main():
|
||||
# 标题
|
||||
st.markdown('<h1 class="main-title">🧬 LNP 配方优化系统</h1>', unsafe_allow_html=True)
|
||||
st.markdown('<p class="sub-title">基于深度学习的脂质纳米颗粒配方智能优选</p>', unsafe_allow_html=True)
|
||||
|
||||
# 检查 API 状态
|
||||
api_online = check_api_status()
|
||||
|
||||
# ========== 侧边栏 ==========
|
||||
with st.sidebar:
|
||||
st.header("⚙️ 参数设置")
|
||||
|
||||
# API 状态
|
||||
if api_online:
|
||||
st.success("🟢 API 服务在线")
|
||||
else:
|
||||
st.error("🔴 API 服务离线")
|
||||
st.info("请先启动 API 服务:\n```\nuvicorn app.api:app --port 8000\n```")
|
||||
|
||||
st.divider()
|
||||
|
||||
# SMILES 输入
|
||||
st.subheader("🔬 分子结构")
|
||||
smiles_input = st.text_area(
|
||||
"输入阳离子脂质 SMILES",
|
||||
value="",
|
||||
height=100,
|
||||
placeholder="例如: CC(C)NCCNC(C)C",
|
||||
help="输入阳离子脂质的 SMILES 字符串",
|
||||
)
|
||||
|
||||
# 示例 SMILES
|
||||
with st.expander("📋 示例 SMILES"):
|
||||
example_smiles = {
|
||||
"DLin-MC3-DMA": "CC(C)=CCCC(C)=CCCC(C)=CCN(C)CCCCCCCCOC(=O)CCCCCCC/C=C\\CCCCCCCC",
|
||||
"简单胺": "CC(C)NCCNC(C)C",
|
||||
"长链胺": "CCCCCCCCCCCCNCCNCCCCCCCCCCCC",
|
||||
}
|
||||
for name, smi in example_smiles.items():
|
||||
if st.button(f"使用 {name}", key=f"example_{name}"):
|
||||
st.session_state["smiles_input"] = smi
|
||||
st.rerun()
|
||||
|
||||
st.divider()
|
||||
|
||||
# 目标器官选择
|
||||
st.subheader("🎯 目标器官")
|
||||
selected_organ = st.selectbox(
|
||||
"选择优化目标器官",
|
||||
options=AVAILABLE_ORGANS,
|
||||
format_func=lambda x: ORGAN_LABELS.get(x, x),
|
||||
index=0,
|
||||
)
|
||||
|
||||
st.divider()
|
||||
|
||||
# 高级选项
|
||||
with st.expander("🔧 高级选项"):
|
||||
top_k = st.slider(
|
||||
"返回配方数量",
|
||||
min_value=5,
|
||||
max_value=50,
|
||||
value=20,
|
||||
step=5,
|
||||
)
|
||||
|
||||
st.divider()
|
||||
|
||||
# 优化按钮
|
||||
optimize_button = st.button(
|
||||
"🚀 开始配方优选",
|
||||
type="primary",
|
||||
use_container_width=True,
|
||||
disabled=not api_online or not smiles_input.strip(),
|
||||
)
|
||||
|
||||
# ========== 主内容区 ==========
|
||||
|
||||
# 使用 session state 存储结果
|
||||
if "results" not in st.session_state:
|
||||
st.session_state["results"] = None
|
||||
if "results_df" not in st.session_state:
|
||||
st.session_state["results_df"] = None
|
||||
|
||||
# 执行优化
|
||||
if optimize_button and smiles_input.strip():
|
||||
with st.spinner("🔄 正在优化配方，请稍候..."):
|
||||
try:
|
||||
results = call_optimize_api(
|
||||
smiles=smiles_input.strip(),
|
||||
organ=selected_organ,
|
||||
top_k=top_k,
|
||||
)
|
||||
st.session_state["results"] = results
|
||||
st.session_state["results_df"] = format_results_dataframe(results)
|
||||
st.session_state["smiles_used"] = smiles_input.strip()
|
||||
st.session_state["organ_used"] = selected_organ
|
||||
st.success("✅ 优化完成！")
|
||||
except httpx.RequestError as e:
|
||||
st.error(f"❌ API 请求失败: {e}")
|
||||
except Exception as e:
|
||||
st.error(f"❌ 发生错误: {e}")
|
||||
|
||||
# 显示结果
|
||||
if st.session_state["results"] is not None:
|
||||
results = st.session_state["results"]
|
||||
df = st.session_state["results_df"]
|
||||
|
||||
# 结果概览
|
||||
col1, col2, col3 = st.columns(3)
|
||||
|
||||
with col1:
|
||||
st.metric(
|
||||
"目标器官",
|
||||
ORGAN_LABELS.get(results["target_organ"], results["target_organ"]).split(" ")[0],
|
||||
)
|
||||
|
||||
with col2:
|
||||
best_score = results["formulations"][0]["target_biodist"]
|
||||
st.metric(
|
||||
"最优 Biodistribution",
|
||||
f"{best_score:.4f}",
|
||||
)
|
||||
|
||||
with col3:
|
||||
st.metric(
|
||||
"优选配方数",
|
||||
len(results["formulations"]),
|
||||
)
|
||||
|
||||
st.divider()
|
||||
|
||||
# 结果表格
|
||||
st.subheader("📊 优选配方列表")
|
||||
|
||||
# 导出按钮行
|
||||
col_export, col_spacer = st.columns([1, 4])
|
||||
with col_export:
|
||||
csv_content = create_export_csv(
|
||||
df,
|
||||
st.session_state.get("smiles_used", ""),
|
||||
st.session_state.get("organ_used", ""),
|
||||
)
|
||||
st.download_button(
|
||||
label="📥 导出 CSV",
|
||||
data=csv_content,
|
||||
file_name=f"lnp_optimization_{results['target_organ']}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv",
|
||||
mime="text/csv",
|
||||
)
|
||||
|
||||
# 显示表格
|
||||
st.dataframe(
|
||||
df,
|
||||
use_container_width=True,
|
||||
hide_index=True,
|
||||
height=600,
|
||||
)
|
||||
|
||||
# 详细信息
|
||||
with st.expander("🔍 查看最优配方详情"):
|
||||
best = results["formulations"][0]
|
||||
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
with col1:
|
||||
st.markdown("**配方参数**")
|
||||
st.json({
|
||||
"阳离子脂质/mRNA 比例": best["cationic_lipid_to_mrna_ratio"],
|
||||
"阳离子脂质 (mol%)": best["cationic_lipid_mol_ratio"],
|
||||
"磷脂 (mol%)": best["phospholipid_mol_ratio"],
|
||||
"胆固醇 (mol%)": best["cholesterol_mol_ratio"],
|
||||
"PEG 脂质 (mol%)": best["peg_lipid_mol_ratio"],
|
||||
"辅助脂质": best["helper_lipid"],
|
||||
"给药途径": best["route"],
|
||||
})
|
||||
|
||||
with col2:
|
||||
st.markdown("**各器官 Biodistribution 预测**")
|
||||
biodist_df = pd.DataFrame([
|
||||
{"器官": ORGAN_LABELS.get(k, k), "Biodistribution": f"{v:.4f}"}
|
||||
for k, v in best["all_biodist"].items()
|
||||
])
|
||||
st.dataframe(biodist_df, hide_index=True, use_container_width=True)
|
||||
|
||||
else:
|
||||
# 欢迎信息
|
||||
st.info("👈 请在左侧输入 SMILES 并选择目标器官，然后点击「开始配方优选」")
|
||||
|
||||
# 使用说明
|
||||
with st.expander("📖 使用说明"):
|
||||
st.markdown("""
|
||||
### 如何使用
|
||||
|
||||
1. **输入 SMILES**: 在左侧输入框中输入阳离子脂质的 SMILES 字符串
|
||||
2. **选择目标器官**: 选择您希望优化的器官靶向
|
||||
3. **点击优选**: 系统将自动搜索最优配方组合
|
||||
4. **查看结果**: 右侧将显示 Top-20 优选配方
|
||||
5. **导出数据**: 点击导出按钮将结果保存为 CSV 文件
|
||||
|
||||
### 优化参数
|
||||
|
||||
系统会优化以下配方参数:
|
||||
- **阳离子脂质/mRNA 比例**: 0.05 - 0.30
|
||||
- **阳离子脂质 mol 比例**: 0.05 - 0.80
|
||||
- **磷脂 mol 比例**: 0.00 - 0.80
|
||||
- **胆固醇 mol 比例**: 0.00 - 0.80
|
||||
- **PEG 脂质 mol 比例**: 0.00 - 0.05
|
||||
- **辅助脂质**: DOPE / DSPC / DOTAP
|
||||
- **给药途径**: 静脉注射 / 肌肉注射
|
||||
|
||||
### 约束条件
|
||||
|
||||
mol 比例之和 = 1 (阳离子脂质 + 磷脂 + 胆固醇 + PEG 脂质)
|
||||
""")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
532
app/optimize.py
532
app/optimize.py
@ -1,532 +0,0 @@
|
||||
"""
|
||||
配方优化模拟程序
|
||||
|
||||
通过迭代式 Grid Search 寻找最优 LNP 配方，最大化目标器官的 Biodistribution。
|
||||
|
||||
使用方法:
|
||||
python -m app.optimize --smiles "CC(C)..." --organ liver
|
||||
"""
|
||||
|
||||
import itertools
|
||||
from pathlib import Path
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import torch
|
||||
from torch.utils.data import DataLoader
|
||||
from loguru import logger
|
||||
from tqdm import tqdm
|
||||
import typer
|
||||
|
||||
from lnp_ml.config import MODELS_DIR
|
||||
from lnp_ml.dataset import (
|
||||
LNPDataset,
|
||||
LNPDatasetConfig,
|
||||
collate_fn,
|
||||
SMILES_COL,
|
||||
COMP_COLS,
|
||||
HELP_COLS,
|
||||
TARGET_BIODIST,
|
||||
get_phys_cols,
|
||||
get_exp_cols,
|
||||
)
|
||||
from lnp_ml.modeling.predict import load_model
|
||||
|
||||
app = typer.Typer()
|
||||
|
||||
# ============ 参数配置 ============
|
||||
|
||||
# 可用的目标器官
|
||||
AVAILABLE_ORGANS = ["lymph_nodes", "heart", "liver", "spleen", "lung", "kidney", "muscle"]
|
||||
|
||||
# comp token 参数范围
|
||||
COMP_PARAM_RANGES = {
|
||||
"Cationic_Lipid_to_mRNA_weight_ratio": (0.05, 0.30),
|
||||
"Cationic_Lipid_Mol_Ratio": (0.05, 0.80),
|
||||
"Phospholipid_Mol_Ratio": (0.00, 0.80),
|
||||
"Cholesterol_Mol_Ratio": (0.00, 0.80),
|
||||
"PEG_Lipid_Mol_Ratio": (0.00, 0.05),
|
||||
}
|
||||
|
||||
# 最小 step size
|
||||
MIN_STEP_SIZE = 0.01
|
||||
|
||||
# 迭代策略：每个迭代的 step_size
|
||||
ITERATION_STEP_SIZES = [0.10, 0.02, 0.01]
|
||||
|
||||
# Helper lipid 选项
|
||||
HELPER_LIPID_OPTIONS = ["DOPE", "DSPC", "DOTAP"]
|
||||
|
||||
# Route of administration 选项
|
||||
ROUTE_OPTIONS = ["intravenous", "intramuscular"]
|
||||
|
||||
|
||||
@dataclass
|
||||
class Formulation:
|
||||
"""配方数据结构"""
|
||||
# comp token
|
||||
cationic_lipid_to_mrna_ratio: float
|
||||
cationic_lipid_mol_ratio: float
|
||||
phospholipid_mol_ratio: float
|
||||
cholesterol_mol_ratio: float
|
||||
peg_lipid_mol_ratio: float
|
||||
# 离散选项
|
||||
helper_lipid: str = "DOPE"
|
||||
route: str = "intravenous"
|
||||
# 预测结果（填充后）
|
||||
biodist_predictions: Dict[str, float] = field(default_factory=dict)
|
||||
|
||||
def to_dict(self) -> Dict:
|
||||
"""转换为字典"""
|
||||
return {
|
||||
"Cationic_Lipid_to_mRNA_weight_ratio": self.cationic_lipid_to_mrna_ratio,
|
||||
"Cationic_Lipid_Mol_Ratio": self.cationic_lipid_mol_ratio,
|
||||
"Phospholipid_Mol_Ratio": self.phospholipid_mol_ratio,
|
||||
"Cholesterol_Mol_Ratio": self.cholesterol_mol_ratio,
|
||||
"PEG_Lipid_Mol_Ratio": self.peg_lipid_mol_ratio,
|
||||
"helper_lipid": self.helper_lipid,
|
||||
"route": self.route,
|
||||
}
|
||||
|
||||
def get_biodist(self, organ: str) -> float:
|
||||
"""获取指定器官的 biodistribution 预测值"""
|
||||
col = f"Biodistribution_{organ}"
|
||||
return self.biodist_predictions.get(col, 0.0)
|
||||
|
||||
|
||||
def generate_grid_values(
|
||||
center: float,
|
||||
step_size: float,
|
||||
min_val: float,
|
||||
max_val: float,
|
||||
radius: int = 2,
|
||||
) -> List[float]:
|
||||
"""
|
||||
围绕中心点生成网格值。
|
||||
|
||||
Args:
|
||||
center: 中心值
|
||||
step_size: 步长
|
||||
min_val: 最小值
|
||||
max_val: 最大值
|
||||
radius: 扩展半径（生成 2*radius+1 个点）
|
||||
|
||||
Returns:
|
||||
网格值列表
|
||||
"""
|
||||
values = []
|
||||
for i in range(-radius, radius + 1):
|
||||
val = center + i * step_size
|
||||
if min_val <= val <= max_val:
|
||||
values.append(round(val, 4))
|
||||
return sorted(set(values))
|
||||
|
||||
|
||||
def generate_initial_grid(step_size: float) -> List[Tuple[float, float, float, float, float]]:
|
||||
"""
|
||||
生成初始搜索网格（满足 mol ratio 和为 1 的约束）。
|
||||
|
||||
Returns:
|
||||
List of (cationic_ratio, cationic_mol, phospholipid_mol, cholesterol_mol, peg_mol)
|
||||
"""
|
||||
grid = []
|
||||
|
||||
# Cationic_Lipid_to_mRNA_weight_ratio
|
||||
weight_ratios = np.arange(0.05, 0.31, step_size)
|
||||
|
||||
# PEG: 单独处理，范围很小
|
||||
peg_values = np.arange(0.00, 0.06, MIN_STEP_SIZE) # PEG 始终用 0.01 步长
|
||||
|
||||
# 其他三个 mol ratio 需要满足和为 1 - PEG
|
||||
mol_step = step_size
|
||||
|
||||
for weight_ratio in weight_ratios:
|
||||
for peg in peg_values:
|
||||
remaining = 1.0 - peg
|
||||
# 生成满足约束的组合
|
||||
for cationic_mol in np.arange(0.05, min(0.81, remaining + 0.001), mol_step):
|
||||
for phospholipid_mol in np.arange(0.00, min(0.81, remaining - cationic_mol + 0.001), mol_step):
|
||||
cholesterol_mol = remaining - cationic_mol - phospholipid_mol
|
||||
# 检查约束
|
||||
if 0.00 <= cholesterol_mol <= 0.80:
|
||||
grid.append((
|
||||
round(weight_ratio, 4),
|
||||
round(cationic_mol, 4),
|
||||
round(phospholipid_mol, 4),
|
||||
round(cholesterol_mol, 4),
|
||||
round(peg, 4),
|
||||
))
|
||||
|
||||
return grid
|
||||
|
||||
|
||||
def generate_refined_grid(
|
||||
seeds: List[Formulation],
|
||||
step_size: float,
|
||||
radius: int = 2,
|
||||
) -> List[Tuple[float, float, float, float, float]]:
|
||||
"""
|
||||
围绕种子点生成精细化网格。
|
||||
|
||||
Args:
|
||||
seeds: 种子配方列表
|
||||
step_size: 步长
|
||||
radius: 扩展半径
|
||||
|
||||
Returns:
|
||||
新的网格点列表
|
||||
"""
|
||||
grid_set = set()
|
||||
|
||||
for seed in seeds:
|
||||
# Weight ratio
|
||||
weight_ratios = generate_grid_values(
|
||||
seed.cationic_lipid_to_mrna_ratio, step_size, 0.05, 0.30, radius
|
||||
)
|
||||
|
||||
# PEG (始终用最小步长)
|
||||
peg_values = generate_grid_values(
|
||||
seed.peg_lipid_mol_ratio, MIN_STEP_SIZE, 0.00, 0.05, radius
|
||||
)
|
||||
|
||||
# Mol ratios
|
||||
cationic_mols = generate_grid_values(
|
||||
seed.cationic_lipid_mol_ratio, step_size, 0.05, 0.80, radius
|
||||
)
|
||||
phospholipid_mols = generate_grid_values(
|
||||
seed.phospholipid_mol_ratio, step_size, 0.00, 0.80, radius
|
||||
)
|
||||
|
||||
for weight_ratio in weight_ratios:
|
||||
for peg in peg_values:
|
||||
remaining = 1.0 - peg
|
||||
for cationic_mol in cationic_mols:
|
||||
for phospholipid_mol in phospholipid_mols:
|
||||
cholesterol_mol = remaining - cationic_mol - phospholipid_mol
|
||||
# 检查约束
|
||||
if (0.05 <= cationic_mol <= 0.80 and
|
||||
0.00 <= phospholipid_mol <= 0.80 and
|
||||
0.00 <= cholesterol_mol <= 0.80 and
|
||||
0.00 <= peg <= 0.05):
|
||||
grid_set.add((
|
||||
round(weight_ratio, 4),
|
||||
round(cationic_mol, 4),
|
||||
round(phospholipid_mol, 4),
|
||||
round(cholesterol_mol, 4),
|
||||
round(peg, 4),
|
||||
))
|
||||
|
||||
return list(grid_set)
|
||||
|
||||
|
||||
def create_dataframe_from_formulations(
|
||||
smiles: str,
|
||||
grid: List[Tuple[float, float, float, float, float]],
|
||||
helper_lipids: List[str],
|
||||
routes: List[str],
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
从配方网格创建 DataFrame。
|
||||
|
||||
使用固定的 phys token（Pure, Microfluidic, mRNA, FFL）和 exp token（Mouse, body, luminescence）。
|
||||
"""
|
||||
rows = []
|
||||
|
||||
for comp_values in grid:
|
||||
for helper in helper_lipids:
|
||||
for route in routes:
|
||||
row = {
|
||||
SMILES_COL: smiles,
|
||||
# comp token
|
||||
"Cationic_Lipid_to_mRNA_weight_ratio": comp_values[0],
|
||||
"Cationic_Lipid_Mol_Ratio": comp_values[1],
|
||||
"Phospholipid_Mol_Ratio": comp_values[2],
|
||||
"Cholesterol_Mol_Ratio": comp_values[3],
|
||||
"PEG_Lipid_Mol_Ratio": comp_values[4],
|
||||
# phys token (固定值)
|
||||
"Purity_Pure": 1.0,
|
||||
"Purity_Crude": 0.0,
|
||||
"Mix_type_Microfluidic": 1.0,
|
||||
"Mix_type_Pipetting": 0.0,
|
||||
"Cargo_type_mRNA": 1.0,
|
||||
"Cargo_type_pDNA": 0.0,
|
||||
"Cargo_type_siRNA": 0.0,
|
||||
"Target_or_delivered_gene_FFL": 1.0,
|
||||
"Target_or_delivered_gene_Peptide_barcode": 0.0,
|
||||
"Target_or_delivered_gene_hEPO": 0.0,
|
||||
"Target_or_delivered_gene_FVII": 0.0,
|
||||
"Target_or_delivered_gene_GFP": 0.0,
|
||||
# help token
|
||||
"Helper_lipid_ID_DOPE": 1.0 if helper == "DOPE" else 0.0,
|
||||
"Helper_lipid_ID_DOTAP": 1.0 if helper == "DOTAP" else 0.0,
|
||||
"Helper_lipid_ID_DSPC": 1.0 if helper == "DSPC" else 0.0,
|
||||
"Helper_lipid_ID_MDOA": 0.0, # 不使用
|
||||
# exp token (固定值)
|
||||
"Model_type_Mouse": 1.0,
|
||||
"Delivery_target_body": 1.0,
|
||||
f"Route_of_administration_{route}": 1.0,
|
||||
"Batch_or_individual_or_barcoded_Individual": 1.0,
|
||||
"Value_name_luminescence": 1.0,
|
||||
# 存储配方元信息
|
||||
"_helper_lipid": helper,
|
||||
"_route": route,
|
||||
}
|
||||
# 其他 exp token 默认为 0
|
||||
for col in get_exp_cols():
|
||||
if col not in row:
|
||||
row[col] = 0.0
|
||||
|
||||
rows.append(row)
|
||||
|
||||
return pd.DataFrame(rows)
|
||||
|
||||
|
||||
def predict_biodist(
|
||||
model: torch.nn.Module,
|
||||
df: pd.DataFrame,
|
||||
device: torch.device,
|
||||
batch_size: int = 256,
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
使用模型预测 biodistribution。
|
||||
|
||||
Returns:
|
||||
添加了预测列的 DataFrame
|
||||
"""
|
||||
dataset = LNPDataset(df)
|
||||
dataloader = DataLoader(
|
||||
dataset, batch_size=batch_size, shuffle=False, collate_fn=collate_fn
|
||||
)
|
||||
|
||||
all_biodist_preds = []
|
||||
|
||||
with torch.no_grad():
|
||||
for batch in dataloader:
|
||||
smiles = batch["smiles"]
|
||||
tabular = {k: v.to(device) for k, v in batch["tabular"].items()}
|
||||
|
||||
outputs = model(smiles, tabular)
|
||||
|
||||
# biodist 输出是 softmax 后的概率分布 [B, 7]
|
||||
biodist_pred = outputs["biodist"].cpu().numpy()
|
||||
all_biodist_preds.append(biodist_pred)
|
||||
|
||||
biodist_preds = np.concatenate(all_biodist_preds, axis=0)
|
||||
|
||||
# 添加到 DataFrame
|
||||
for i, col in enumerate(TARGET_BIODIST):
|
||||
df[f"pred_{col}"] = biodist_preds[:, i]
|
||||
|
||||
return df
|
||||
|
||||
|
||||
def select_top_k(
|
||||
df: pd.DataFrame,
|
||||
organ: str,
|
||||
k: int = 20,
|
||||
) -> List[Formulation]:
|
||||
"""
|
||||
选择 top-k 配方。
|
||||
|
||||
Args:
|
||||
df: 包含预测结果的 DataFrame
|
||||
organ: 目标器官
|
||||
k: 选择数量
|
||||
|
||||
Returns:
|
||||
Top-k 配方列表
|
||||
"""
|
||||
pred_col = f"pred_Biodistribution_{organ}"
|
||||
if pred_col not in df.columns:
|
||||
raise ValueError(f"Prediction column {pred_col} not found")
|
||||
|
||||
# 排序并去重
|
||||
df_sorted = df.sort_values(pred_col, ascending=False)
|
||||
|
||||
# 创建配方对象
|
||||
formulations = []
|
||||
seen = set()
|
||||
|
||||
for _, row in df_sorted.iterrows():
|
||||
key = (
|
||||
row["Cationic_Lipid_to_mRNA_weight_ratio"],
|
||||
row["Cationic_Lipid_Mol_Ratio"],
|
||||
row["Phospholipid_Mol_Ratio"],
|
||||
row["Cholesterol_Mol_Ratio"],
|
||||
row["PEG_Lipid_Mol_Ratio"],
|
||||
row["_helper_lipid"],
|
||||
row["_route"],
|
||||
)
|
||||
|
||||
if key not in seen:
|
||||
seen.add(key)
|
||||
formulation = Formulation(
|
||||
cationic_lipid_to_mrna_ratio=row["Cationic_Lipid_to_mRNA_weight_ratio"],
|
||||
cationic_lipid_mol_ratio=row["Cationic_Lipid_Mol_Ratio"],
|
||||
phospholipid_mol_ratio=row["Phospholipid_Mol_Ratio"],
|
||||
cholesterol_mol_ratio=row["Cholesterol_Mol_Ratio"],
|
||||
peg_lipid_mol_ratio=row["PEG_Lipid_Mol_Ratio"],
|
||||
helper_lipid=row["_helper_lipid"],
|
||||
route=row["_route"],
|
||||
biodist_predictions={
|
||||
col: row[f"pred_{col}"] for col in TARGET_BIODIST
|
||||
},
|
||||
)
|
||||
formulations.append(formulation)
|
||||
|
||||
if len(formulations) >= k:
|
||||
break
|
||||
|
||||
return formulations
|
||||
|
||||
|
||||
def optimize(
|
||||
smiles: str,
|
||||
organ: str,
|
||||
model: torch.nn.Module,
|
||||
device: torch.device,
|
||||
top_k: int = 20,
|
||||
batch_size: int = 256,
|
||||
) -> List[Formulation]:
|
||||
"""
|
||||
执行配方优化。
|
||||
|
||||
Args:
|
||||
smiles: SMILES 字符串
|
||||
organ: 目标器官
|
||||
model: 训练好的模型
|
||||
device: 计算设备
|
||||
top_k: 每轮保留的最优配方数
|
||||
batch_size: 预测批次大小
|
||||
|
||||
Returns:
|
||||
最终 top-k 配方列表
|
||||
"""
|
||||
logger.info(f"Starting optimization for organ: {organ}")
|
||||
logger.info(f"SMILES: {smiles}")
|
||||
|
||||
seeds = None
|
||||
|
||||
for iteration, step_size in enumerate(ITERATION_STEP_SIZES):
|
||||
logger.info(f"\n{'='*60}")
|
||||
logger.info(f"Iteration {iteration + 1}/{len(ITERATION_STEP_SIZES)}, step_size={step_size}")
|
||||
logger.info(f"{'='*60}")
|
||||
|
||||
# 生成网格
|
||||
if seeds is None:
|
||||
# 第一次迭代：生成完整初始网格
|
||||
logger.info("Generating initial grid...")
|
||||
grid = generate_initial_grid(step_size)
|
||||
else:
|
||||
# 后续迭代：围绕种子点精细化
|
||||
logger.info(f"Generating refined grid around {len(seeds)} seeds...")
|
||||
grid = generate_refined_grid(seeds, step_size, radius=2)
|
||||
|
||||
logger.info(f"Grid size: {len(grid)} comp combinations")
|
||||
|
||||
# 扩展到所有 helper lipid 和 route 组合
|
||||
total_combinations = len(grid) * len(HELPER_LIPID_OPTIONS) * len(ROUTE_OPTIONS)
|
||||
logger.info(f"Total combinations: {total_combinations}")
|
||||
|
||||
# 创建 DataFrame
|
||||
df = create_dataframe_from_formulations(
|
||||
smiles, grid, HELPER_LIPID_OPTIONS, ROUTE_OPTIONS
|
||||
)
|
||||
|
||||
# 预测
|
||||
logger.info("Running predictions...")
|
||||
df = predict_biodist(model, df, device, batch_size)
|
||||
|
||||
# 选择 top-k
|
||||
seeds = select_top_k(df, organ, top_k)
|
||||
|
||||
# 显示当前最优
|
||||
best = seeds[0]
|
||||
logger.info(f"Current best Biodistribution_{organ}: {best.get_biodist(organ):.4f}")
|
||||
logger.info(f"Best formulation: {best.to_dict()}")
|
||||
|
||||
return seeds
|
||||
|
||||
|
||||
def format_results(formulations: List[Formulation], organ: str) -> pd.DataFrame:
|
||||
"""格式化结果为 DataFrame"""
|
||||
rows = []
|
||||
for i, f in enumerate(formulations):
|
||||
row = {
|
||||
"rank": i + 1,
|
||||
f"Biodistribution_{organ}": f.get_biodist(organ),
|
||||
**f.to_dict(),
|
||||
}
|
||||
# 添加其他器官的预测
|
||||
for col in TARGET_BIODIST:
|
||||
if col != f"Biodistribution_{organ}":
|
||||
row[col] = f.biodist_predictions.get(col, 0.0)
|
||||
rows.append(row)
|
||||
return pd.DataFrame(rows)
|
||||
|
||||
|
||||
@app.command()
|
||||
def main(
|
||||
smiles: str = typer.Option(..., "--smiles", "-s", help="Cationic lipid SMILES string"),
|
||||
organ: str = typer.Option(..., "--organ", "-o", help=f"Target organ: {AVAILABLE_ORGANS}"),
|
||||
model_path: Path = typer.Option(
|
||||
MODELS_DIR / "final" / "model.pt",
|
||||
"--model", "-m",
|
||||
help="Path to trained model checkpoint"
|
||||
),
|
||||
output_path: Optional[Path] = typer.Option(
|
||||
None, "--output", "-O",
|
||||
help="Output CSV path (optional)"
|
||||
),
|
||||
top_k: int = typer.Option(20, "--top-k", "-k", help="Number of top formulations to return"),
|
||||
batch_size: int = typer.Option(256, "--batch-size", "-b", help="Prediction batch size"),
|
||||
device: str = typer.Option("cuda" if torch.cuda.is_available() else "cpu", "--device", "-d", help="Device"),
|
||||
):
|
||||
"""
|
||||
配方优化程序：寻找最大化目标器官 Biodistribution 的最优 LNP 配方。
|
||||
|
||||
示例:
|
||||
python -m app.optimize --smiles "CC(C)..." --organ liver
|
||||
python -m app.optimize -s "CC(C)..." -o spleen -k 10
|
||||
"""
|
||||
# 验证器官
|
||||
if organ not in AVAILABLE_ORGANS:
|
||||
logger.error(f"Invalid organ: {organ}. Available: {AVAILABLE_ORGANS}")
|
||||
raise typer.Exit(1)
|
||||
|
||||
# 加载模型
|
||||
logger.info(f"Loading model from {model_path}")
|
||||
device = torch.device(device)
|
||||
model = load_model(model_path, device)
|
||||
|
||||
# 执行优化
|
||||
results = optimize(
|
||||
smiles=smiles,
|
||||
organ=organ,
|
||||
model=model,
|
||||
device=device,
|
||||
top_k=top_k,
|
||||
batch_size=batch_size,
|
||||
)
|
||||
|
||||
# 格式化并显示结果
|
||||
df_results = format_results(results, organ)
|
||||
|
||||
logger.info(f"\n{'='*60}")
|
||||
logger.info(f"TOP {top_k} FORMULATIONS FOR {organ.upper()}")
|
||||
logger.info(f"{'='*60}")
|
||||
print(df_results.to_string(index=False))
|
||||
|
||||
# 保存结果
|
||||
if output_path:
|
||||
df_results.to_csv(output_path, index=False)
|
||||
logger.success(f"Results saved to {output_path}")
|
||||
|
||||
return df_results
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
app()
|
||||
|
||||
@ -1,89 +0,0 @@
|
||||
# Feature columns configuration (final training)
|
||||
|
||||
# SMILES
|
||||
smiles
|
||||
|
||||
# comp token [5]
|
||||
Cationic_Lipid_to_mRNA_weight_ratio
|
||||
Cationic_Lipid_Mol_Ratio
|
||||
Phospholipid_Mol_Ratio
|
||||
Cholesterol_Mol_Ratio
|
||||
PEG_Lipid_Mol_Ratio
|
||||
|
||||
# phys token [12]
|
||||
Purity_Pure
|
||||
Purity_Crude
|
||||
Mix_type_Microfluidic
|
||||
Mix_type_Pipetting
|
||||
Cargo_type_mRNA
|
||||
Cargo_type_pDNA
|
||||
Cargo_type_siRNA
|
||||
Target_or_delivered_gene_FFL
|
||||
Target_or_delivered_gene_Peptide_barcode
|
||||
Target_or_delivered_gene_hEPO
|
||||
Target_or_delivered_gene_FVII
|
||||
Target_or_delivered_gene_GFP
|
||||
|
||||
# help token [4]
|
||||
Helper_lipid_ID_DOPE
|
||||
Helper_lipid_ID_DOTAP
|
||||
Helper_lipid_ID_DSPC
|
||||
Helper_lipid_ID_MDOA
|
||||
|
||||
# exp token [32]
|
||||
Model_type_A549
|
||||
Model_type_BDMC
|
||||
Model_type_BMDM
|
||||
Model_type_HBEC_ALI
|
||||
Model_type_HEK293T
|
||||
Model_type_HeLa
|
||||
Model_type_IGROV1
|
||||
Model_type_Mouse
|
||||
Model_type_RAW264p7
|
||||
Delivery_target_body
|
||||
Delivery_target_dendritic_cell
|
||||
Delivery_target_generic_cell
|
||||
Delivery_target_liver
|
||||
Delivery_target_lung
|
||||
Delivery_target_lung_epithelium
|
||||
Delivery_target_macrophage
|
||||
Delivery_target_muscle
|
||||
Delivery_target_spleen
|
||||
Route_of_administration_in_vitro
|
||||
Route_of_administration_intramuscular
|
||||
Route_of_administration_intratracheal
|
||||
Route_of_administration_intravenous
|
||||
Batch_or_individual_or_barcoded_Barcoded
|
||||
Batch_or_individual_or_barcoded_Individual
|
||||
Value_name_log_luminescence
|
||||
Value_name_luminescence
|
||||
Value_name_FFL_silencing
|
||||
Value_name_Peptide_abundance
|
||||
Value_name_hEPO
|
||||
Value_name_FVII_silencing
|
||||
Value_name_GFP_delivery
|
||||
Value_name_Discretized_luminescence
|
||||
|
||||
# Targets
|
||||
## Regression
|
||||
size
|
||||
quantified_delivery
|
||||
## PDI classification
|
||||
PDI_0_0to0_2
|
||||
PDI_0_2to0_3
|
||||
PDI_0_3to0_4
|
||||
PDI_0_4to0_5
|
||||
## EE classification
|
||||
Encapsulation_Efficiency_EE<50
|
||||
Encapsulation_Efficiency_50<=EE<80
|
||||
Encapsulation_Efficiency_80<EE<=100
|
||||
## Toxic
|
||||
toxic
|
||||
## Biodistribution
|
||||
Biodistribution_lymph_nodes
|
||||
Biodistribution_heart
|
||||
Biodistribution_liver
|
||||
Biodistribution_spleen
|
||||
Biodistribution_lung
|
||||
Biodistribution_kidney
|
||||
Biodistribution_muscle
|
||||
Binary file not shown.
Binary file not shown.
@ -1,720 +0,0 @@
|
||||
{
|
||||
"train": [
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||||
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||||
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|
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|
||||
{
|
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|
||||
{
|
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||||
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||||
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|
||||
{
|
||||
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||||
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||||
},
|
||||
{
|
||||
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||||
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|
||||
{
|
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||||
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|
||||
{
|
||||
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||||
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|
||||
{
|
||||
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||||
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||||
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||||
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||||
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|
||||
{
|
||||
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|
||||
{
|
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||||
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||||
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||||
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||||
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|
||||
{
|
||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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|
||||
{
|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
{
|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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- pypi: https://files.pythonhosted.org/packages/b6/cb/b86984bed139586d01532a587464b5805f12e397594f19f931c4c2fbfa61/tenacity-9.0.0-py3-none-any.whl
|
||||
name: tenacity
|
||||
version: 9.0.0
|
||||
sha256: 93de0c98785b27fcf659856aa9f54bfbd399e29969b0621bc7f762bd441b4539
|
||||
requires_dist:
|
||||
- reno ; extra == 'doc'
|
||||
- sphinx ; extra == 'doc'
|
||||
- pytest ; extra == 'test'
|
||||
- tornado>=4.5 ; extra == 'test'
|
||||
- typeguard ; extra == 'test'
|
||||
requires_python: '>=3.8'
|
||||
- pypi: https://files.pythonhosted.org/packages/44/71/f3e7c9b2ab67e28c572ab4e9d5fa3499e0d252650f96d8a3a03e26677f53/tensorboardX-2.6.2.2-py2.py3-none-any.whl
|
||||
name: tensorboardx
|
||||
version: 2.6.2.2
|
||||
@ -2652,11 +2167,6 @@ packages:
|
||||
purls: []
|
||||
size: 3125484
|
||||
timestamp: 1763055028377
|
||||
- pypi: https://files.pythonhosted.org/packages/44/6f/7120676b6d73228c96e17f1f794d8ab046fc910d781c8d151120c3f1569e/toml-0.10.2-py2.py3-none-any.whl
|
||||
name: toml
|
||||
version: 0.10.2
|
||||
sha256: 806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b
|
||||
requires_python: '>=2.6,!=3.0.*,!=3.1.*,!=3.2.*'
|
||||
- pypi: https://files.pythonhosted.org/packages/a9/71/45aac46b75742e08d2d6f9fc2b612223b5e36115b8b2ed673b23c21b5387/torch-2.4.1-cp38-cp38-manylinux1_x86_64.whl
|
||||
name: torch
|
||||
version: 2.4.1
|
||||
@ -2709,16 +2219,6 @@ packages:
|
||||
- opt-einsum>=3.3 ; extra == 'opt-einsum'
|
||||
- optree>=0.11.0 ; extra == 'optree'
|
||||
requires_python: '>=3.8.0'
|
||||
- pypi: https://files.pythonhosted.org/packages/22/55/b78a464de78051a30599ceb6983b01d8f732e6f69bf37b4ed07f642ac0fc/tornado-6.4.2-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
|
||||
name: tornado
|
||||
version: 6.4.2
|
||||
sha256: bca9eb02196e789c9cb5c3c7c0f04fb447dc2adffd95265b2c7223a8a615ccbf
|
||||
requires_python: '>=3.8'
|
||||
- pypi: https://files.pythonhosted.org/packages/26/7e/71f604d8cea1b58f82ba3590290b66da1e72d840aeb37e0d5f7291bd30db/tornado-6.4.2-cp38-abi3-macosx_10_9_universal2.whl
|
||||
name: tornado
|
||||
version: 6.4.2
|
||||
sha256: e828cce1123e9e44ae2a50a9de3055497ab1d0aeb440c5ac23064d9e44880da1
|
||||
requires_python: '>=3.8'
|
||||
- conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.1-pyhd8ed1ab_0.conda
|
||||
sha256: 5673b7104350a6998cb86cccf1d0058217d86950e8d6c927d8530606028edb1d
|
||||
md5: 4085c9db273a148e149c03627350e22c
|
||||
@ -2838,29 +2338,6 @@ packages:
|
||||
- pysocks>=1.5.6,!=1.5.7,<2.0 ; extra == 'socks'
|
||||
- zstandard>=0.18.0 ; extra == 'zstd'
|
||||
requires_python: '>=3.8'
|
||||
- pypi: https://files.pythonhosted.org/packages/98/79/2e2620337ef1e4ef7a058b351603b765f59ac28e6e3ac7c5e7cdee9ea1ab/uvicorn-0.33.0-py3-none-any.whl
|
||||
name: uvicorn
|
||||
version: 0.33.0
|
||||
sha256: 2c30de4aeea83661a520abab179b24084a0019c0c1bbe137e5409f741cbde5f8
|
||||
requires_dist:
|
||||
- click>=7.0
|
||||
- h11>=0.8
|
||||
- typing-extensions>=4.0 ; python_full_version < '3.11'
|
||||
- colorama>=0.4 ; sys_platform == 'win32' and extra == 'standard'
|
||||
- httptools>=0.6.3 ; extra == 'standard'
|
||||
- python-dotenv>=0.13 ; extra == 'standard'
|
||||
- pyyaml>=5.1 ; extra == 'standard'
|
||||
- uvloop>=0.14.0,!=0.15.0,!=0.15.1 ; platform_python_implementation != 'PyPy' and sys_platform != 'cygwin' and sys_platform != 'win32' and extra == 'standard'
|
||||
- watchfiles>=0.13 ; extra == 'standard'
|
||||
- websockets>=10.4 ; extra == 'standard'
|
||||
requires_python: '>=3.8'
|
||||
- pypi: https://files.pythonhosted.org/packages/01/d2/c8931ff840a7e5bd5dcb93f2bb2a1fd18faf8312e9f7f53ff1cf76ecc8ed/watchdog-4.0.2-py3-none-manylinux2014_x86_64.whl
|
||||
name: watchdog
|
||||
version: 4.0.2
|
||||
sha256: c0b14488bd336c5b1845cee83d3e631a1f8b4e9c5091ec539406e4a324f882d8
|
||||
requires_dist:
|
||||
- pyyaml>=3.10 ; extra == 'watchmedo'
|
||||
requires_python: '>=3.8'
|
||||
- pypi: https://files.pythonhosted.org/packages/fd/21/0a674dfe66e9df9072c46269c882e9f901d36d987d8ea50ead033a9c1e01/werkzeug-2.3.8-py3-none-any.whl
|
||||
name: werkzeug
|
||||
version: 2.3.8
|
||||
|
||||
@ -24,7 +24,3 @@ openpyxl = ">=3.1.5, <4"
|
||||
python-dotenv = ">=1.0.1, <2"
|
||||
pyarrow = ">=17.0.0, <18"
|
||||
fastparquet = ">=2024.2.0, <2025"
|
||||
fastapi = ">=0.124.4, <0.125"
|
||||
streamlit = ">=1.40.1, <2"
|
||||
httpx = ">=0.28.1, <0.29"
|
||||
uvicorn = ">=0.33.0, <0.34"
|
||||
|
||||
@ -1,153 +0,0 @@
|
||||
"""最终训练数据处理脚本：train:val = 9:1，无测试集"""
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
import pandas as pd
|
||||
import typer
|
||||
from loguru import logger
|
||||
|
||||
from lnp_ml.config import INTERIM_DATA_DIR, PROCESSED_DATA_DIR
|
||||
from lnp_ml.dataset import (
|
||||
process_dataframe,
|
||||
SMILES_COL,
|
||||
COMP_COLS,
|
||||
HELP_COLS,
|
||||
TARGET_REGRESSION,
|
||||
TARGET_CLASSIFICATION_PDI,
|
||||
TARGET_CLASSIFICATION_EE,
|
||||
TARGET_TOXIC,
|
||||
TARGET_BIODIST,
|
||||
get_phys_cols,
|
||||
get_exp_cols,
|
||||
)
|
||||
|
||||
app = typer.Typer()
|
||||
|
||||
|
||||
@app.command()
|
||||
def main(
|
||||
input_path: Path = INTERIM_DATA_DIR / "internal_corrected.csv",
|
||||
output_dir: Path = PROCESSED_DATA_DIR / "final",
|
||||
train_ratio: float = 0.9,
|
||||
seed: int = 42,
|
||||
):
|
||||
"""
|
||||
处理原始数据并划分训练/验证集（无测试集）。
|
||||
|
||||
用于最终训练，使用所有数据。
|
||||
|
||||
输出文件:
|
||||
- final/train.parquet: 训练集 (90%)
|
||||
- final/val.parquet: 验证集 (10%)
|
||||
- final/feature_columns.txt: 特征列名配置
|
||||
"""
|
||||
logger.info(f"Loading data from {input_path}")
|
||||
df = pd.read_csv(input_path)
|
||||
logger.info(f"Loaded {len(df)} samples")
|
||||
|
||||
# 处理数据
|
||||
logger.info("Processing dataframe...")
|
||||
df = process_dataframe(df)
|
||||
|
||||
# 定义要保留的列
|
||||
phys_cols = get_phys_cols()
|
||||
exp_cols = get_exp_cols()
|
||||
|
||||
keep_cols = (
|
||||
[SMILES_COL]
|
||||
+ COMP_COLS
|
||||
+ phys_cols
|
||||
+ HELP_COLS
|
||||
+ exp_cols
|
||||
+ TARGET_REGRESSION
|
||||
+ TARGET_CLASSIFICATION_PDI
|
||||
+ TARGET_CLASSIFICATION_EE
|
||||
+ [TARGET_TOXIC]
|
||||
+ TARGET_BIODIST
|
||||
)
|
||||
|
||||
# 只保留存在的列
|
||||
keep_cols = [c for c in keep_cols if c in df.columns]
|
||||
df = df[keep_cols]
|
||||
|
||||
# 随机打乱并划分
|
||||
logger.info(f"Splitting dataset (train:val = {train_ratio}:{1-train_ratio:.1f})...")
|
||||
df = df.sample(frac=1, random_state=seed).reset_index(drop=True)
|
||||
|
||||
n = len(df)
|
||||
n_train = int(n * train_ratio)
|
||||
|
||||
train_df = df.iloc[:n_train]
|
||||
val_df = df.iloc[n_train:]
|
||||
|
||||
logger.info(f"Train: {len(train_df)}, Val: {len(val_df)}")
|
||||
|
||||
# 保存
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
train_path = output_dir / "train.parquet"
|
||||
val_path = output_dir / "val.parquet"
|
||||
|
||||
train_df.to_parquet(train_path, index=False)
|
||||
val_df.to_parquet(val_path, index=False)
|
||||
|
||||
logger.success(f"Saved train to {train_path}")
|
||||
logger.success(f"Saved val to {val_path}")
|
||||
|
||||
# 保存列名配置
|
||||
config_path = output_dir / "feature_columns.txt"
|
||||
with open(config_path, "w") as f:
|
||||
f.write("# Feature columns configuration (final training)\n\n")
|
||||
f.write(f"# SMILES\n{SMILES_COL}\n\n")
|
||||
f.write(f"# comp token [{len(COMP_COLS)}]\n")
|
||||
f.write("\n".join(COMP_COLS) + "\n\n")
|
||||
f.write(f"# phys token [{len(phys_cols)}]\n")
|
||||
f.write("\n".join(phys_cols) + "\n\n")
|
||||
f.write(f"# help token [{len(HELP_COLS)}]\n")
|
||||
f.write("\n".join(HELP_COLS) + "\n\n")
|
||||
f.write(f"# exp token [{len(exp_cols)}]\n")
|
||||
f.write("\n".join(exp_cols) + "\n\n")
|
||||
f.write("# Targets\n")
|
||||
f.write("## Regression\n")
|
||||
f.write("\n".join(TARGET_REGRESSION) + "\n")
|
||||
f.write("## PDI classification\n")
|
||||
f.write("\n".join(TARGET_CLASSIFICATION_PDI) + "\n")
|
||||
f.write("## EE classification\n")
|
||||
f.write("\n".join(TARGET_CLASSIFICATION_EE) + "\n")
|
||||
f.write("## Toxic\n")
|
||||
f.write(f"{TARGET_TOXIC}\n")
|
||||
f.write("## Biodistribution\n")
|
||||
f.write("\n".join(TARGET_BIODIST) + "\n")
|
||||
|
||||
logger.success(f"Saved feature config to {config_path}")
|
||||
|
||||
# 打印统计信息
|
||||
logger.info("\n=== Dataset Statistics ===")
|
||||
logger.info(f"Total samples: {n}")
|
||||
logger.info(f"SMILES unique: {df[SMILES_COL].nunique()}")
|
||||
|
||||
# 缺失值统计
|
||||
logger.info("\nMissing values in targets:")
|
||||
for col in TARGET_REGRESSION + [TARGET_TOXIC]:
|
||||
if col in df.columns:
|
||||
missing = df[col].isna().sum()
|
||||
logger.info(f" {col}: {missing} ({100*missing/n:.1f}%)")
|
||||
|
||||
# PDI 分布
|
||||
if all(c in df.columns for c in TARGET_CLASSIFICATION_PDI):
|
||||
pdi_sum = df[TARGET_CLASSIFICATION_PDI].sum()
|
||||
logger.info(f"\nPDI distribution:")
|
||||
for col, count in pdi_sum.items():
|
||||
logger.info(f" {col}: {int(count)}")
|
||||
|
||||
# EE 分布
|
||||
if all(c in df.columns for c in TARGET_CLASSIFICATION_EE):
|
||||
ee_sum = df[TARGET_CLASSIFICATION_EE].sum()
|
||||
logger.info(f"\nEE distribution:")
|
||||
for col, count in ee_sum.items():
|
||||
logger.info(f" {col}: {int(count)}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
app()
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user