mirror of
https://github.com/RYDE-WORK/lnp_ml.git
synced 2026-03-21 01:27:00 +08:00
802 lines
30 KiB
Python
802 lines
30 KiB
Python
"""
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Streamlit 配方优化交互界面
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启动应用:
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streamlit run app/app.py
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Docker 环境变量:
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API_URL: API 服务地址 (默认: http://localhost:8000)
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"""
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import io
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import os
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from datetime import datetime
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import httpx
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import pandas as pd
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import streamlit as st
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# ============ 配置 ============
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# 从环境变量读取 API 地址，支持 Docker 环境
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API_URL = os.environ.get("API_URL", "http://localhost:8000")
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AVAILABLE_ORGANS = [
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"liver",
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"spleen",
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"lung",
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"heart",
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"kidney",
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"muscle",
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"lymph_nodes",
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]
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ORGAN_LABELS = {
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"liver": "肝脏 (Liver)",
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"spleen": "脾脏 (Spleen)",
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"lung": "肺 (Lung)",
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"heart": "心脏 (Heart)",
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"kidney": "肾脏 (Kidney)",
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"muscle": "肌肉 (Muscle)",
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"lymph_nodes": "淋巴结 (Lymph Nodes)",
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}
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AVAILABLE_ROUTES = [
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"intravenous",
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"intramuscular",
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]
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ROUTE_LABELS = {
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"intravenous": "静脉注射 (Intravenous)",
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"intramuscular": "肌肉注射 (Intramuscular)",
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}
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# ============ 页面配置 ============
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st.set_page_config(
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page_title="LNP 配方优化",
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page_icon="🧬",
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layout="wide",
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initial_sidebar_state="expanded",
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)
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# ============ 自定义样式 ============
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st.markdown("""
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<style>
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/* 主标题样式 */
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.main-title {
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font-size: 2.5rem;
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font-weight: 700;
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background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
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-webkit-background-clip: text;
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-webkit-text-fill-color: transparent;
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text-align: center;
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margin-bottom: 0.5rem;
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}
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/* 副标题样式 */
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.sub-title {
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font-size: 1.1rem;
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color: #6c757d;
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text-align: center;
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margin-bottom: 2rem;
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}
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/* 结果卡片 */
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.result-card {
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background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
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border-radius: 12px;
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padding: 1.5rem;
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margin-bottom: 1rem;
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}
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/* 指标高亮 */
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.metric-highlight {
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font-size: 2rem;
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font-weight: 700;
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color: #667eea;
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}
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/* 侧边栏样式 */
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.sidebar-section {
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background: #f8f9fa;
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border-radius: 8px;
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padding: 1rem;
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margin-bottom: 1rem;
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}
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/* 状态指示器 */
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.status-online {
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color: #28a745;
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font-weight: 600;
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}
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.status-offline {
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color: #dc3545;
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font-weight: 600;
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}
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/* 表格样式优化 */
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.dataframe {
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font-size: 0.85rem;
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}
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</style>
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""", unsafe_allow_html=True)
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# ============ 辅助函数 ============
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def check_api_status() -> bool:
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"""检查 API 状态"""
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try:
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with httpx.Client(timeout=5) as client:
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response = client.get(f"{API_URL}/")
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return response.status_code == 200
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except:
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return False
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def call_optimize_api(
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smiles: str,
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organ: str,
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top_k: int = 20,
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num_seeds: int = None,
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top_per_seed: int = 1,
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step_sizes: list = None,
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wr_step_sizes: list = None,
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comp_ranges: dict = None,
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routes: list = None,
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scoring_weights: dict = None,
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) -> dict:
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"""调用优化 API"""
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payload = {
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"smiles": smiles,
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"organ": organ,
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"top_k": top_k,
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"num_seeds": num_seeds,
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"top_per_seed": top_per_seed,
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"step_sizes": step_sizes,
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"wr_step_sizes": wr_step_sizes,
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"comp_ranges": comp_ranges,
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"routes": routes,
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"scoring_weights": scoring_weights,
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}
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with httpx.Client(timeout=600) as client: # 10 分钟超时（自定义参数可能需要更长时间）
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response = client.post(
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f"{API_URL}/optimize",
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json=payload,
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)
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response.raise_for_status()
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return response.json()
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# PDI 分类标签
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PDI_CLASS_LABELS = {
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0: "<0.2 (优)",
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1: "0.2-0.3 (良)",
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2: "0.3-0.4 (中)",
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3: ">0.4 (差)",
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}
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# EE 分类标签
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EE_CLASS_LABELS = {
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0: "<50% (低)",
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1: "50-80% (中)",
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2: ">80% (高)",
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}
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# 毒性分类标签
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TOXIC_CLASS_LABELS = {
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0: "无毒 ✓",
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1: "有毒 ⚠",
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}
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def format_results_dataframe(results: dict, smiles_label: str = None) -> pd.DataFrame:
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"""将 API 结果转换为 DataFrame"""
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formulations = results["formulations"]
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target_organ = results["target_organ"]
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rows = []
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for f in formulations:
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row = {}
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# 如果有 SMILES 标签，添加到首列
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if smiles_label:
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row["SMILES"] = smiles_label
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row.update({
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"排名": f["rank"],
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})
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# 如果有综合评分，显示在排名后面
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if f.get("composite_score") is not None:
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row["综合评分"] = f"{f['composite_score']:.4f}"
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row.update({
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f"{target_organ}分布": f"{f['target_biodist']*100:.8f}%",
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"阳离子脂质/mRNA比例": f["cationic_lipid_to_mrna_ratio"],
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"阳离子脂质(mol)比例": f["cationic_lipid_mol_ratio"],
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"磷脂(mol)比例": f["phospholipid_mol_ratio"],
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"胆固醇(mol)比例": f["cholesterol_mol_ratio"],
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"PEG脂质(mol)比例": f["peg_lipid_mol_ratio"],
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"辅助脂质": f["helper_lipid"],
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"给药途径": f["route"],
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})
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# 添加额外预测值
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if f.get("quantified_delivery") is not None:
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row["量化递送"] = f"{f['quantified_delivery']:.4f}"
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if f.get("unnormalized_delivery") is not None:
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row["总荧光强度"] = f"{f['unnormalized_delivery']:.4f}"
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if f.get("size") is not None:
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row["粒径(nm)"] = f"{f['size']:.1f}"
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if f.get("pdi_class") is not None:
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row["PDI"] = PDI_CLASS_LABELS.get(f["pdi_class"], str(f["pdi_class"]))
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if f.get("ee_class") is not None:
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row["包封率"] = EE_CLASS_LABELS.get(f["ee_class"], str(f["ee_class"]))
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if f.get("toxic_class") is not None:
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row["毒性"] = TOXIC_CLASS_LABELS.get(f["toxic_class"], str(f["toxic_class"]))
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# 添加其他器官的 biodist
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for organ, value in f["all_biodist"].items():
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if organ != target_organ:
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row[f"{organ}分布"] = f"{value*100:.2f}%"
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rows.append(row)
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return pd.DataFrame(rows)
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def create_export_csv(df: pd.DataFrame, smiles: str, organ: str) -> str:
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"""创建导出用的 CSV 内容"""
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# 添加元信息
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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"
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csv_content = df.to_csv(index=False)
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return meta_info + csv_content
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# ============ 主界面 ============
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def main():
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# 标题
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st.markdown('<h1 class="main-title">🧬 LNP 配方优化系统</h1>', unsafe_allow_html=True)
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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)
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# 检查 API 状态
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api_online = check_api_status()
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# ========== 侧边栏 ==========
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with st.sidebar:
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# st.header("⚙️ 参数设置")
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# API 状态
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if api_online:
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st.success("🟢 API 服务在线")
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else:
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st.error("🔴 API 服务离线")
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st.info("请先启动 API 服务:\n```\nuvicorn app.api:app --port 8000\n```")
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# st.divider()
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# SMILES 输入
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st.subheader("🔬 分子结构")
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smiles_input = st.text_area(
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"输入阳离子脂质 SMILES",
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value="",
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height=100,
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placeholder="例如: CC(C)NCCNC(C)C\n多条SMILES用英文逗号分隔: SMI1,SMI2,SMI3",
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help="输入阳离子脂质的 SMILES 字符串。支持多条 SMILES，用英文逗号 (,) 分隔",
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)
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# 示例 SMILES
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# with st.expander("📋 示例 SMILES"):
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# example_smiles = {
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# "DLin-MC3-DMA": "CC(C)=CCCC(C)=CCCC(C)=CCN(C)CCCCCCCCOC(=O)CCCCCCC/C=C\\CCCCCCCC",
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# "简单胺": "CC(C)NCCNC(C)C",
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# "长链胺": "CCCCCCCCCCCCNCCNCCCCCCCCCCCC",
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# }
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# for name, smi in example_smiles.items():
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# if st.button(f"使用 {name}", key=f"example_{name}"):
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# st.session_state["smiles_input"] = smi
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# st.rerun()
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# st.divider()
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# 目标器官选择
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st.subheader("🎯 目标器官")
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selected_organ = st.selectbox(
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"选择优化目标器官",
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options=AVAILABLE_ORGANS,
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format_func=lambda x: ORGAN_LABELS.get(x, x),
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index=0,
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)
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# 给药途径选择
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st.subheader("💉 给药途径")
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selected_routes = st.multiselect(
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"选择给药途径",
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options=AVAILABLE_ROUTES,
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default=AVAILABLE_ROUTES,
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format_func=lambda x: ROUTE_LABELS.get(x, x),
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help="选择要搜索的给药途径，可多选。至少选择一种。",
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)
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if not selected_routes:
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st.warning("⚠️ 请至少选择一种给药途径")
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# 高级选项
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with st.expander("🔧 高级选项"):
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st.markdown("**输出设置**")
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top_k = st.slider(
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"返回配方数量 (top_k)",
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min_value=5,
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max_value=100,
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value=20,
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step=5,
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help="最终返回的最优配方数量",
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)
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st.markdown("**搜索策略**")
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num_seeds = st.slider(
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"种子点数量 (num_seeds)",
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min_value=10,
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max_value=200,
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value=top_k * 5,
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step=10,
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help="第一轮迭代后保留的种子点数量，更多种子点意味着更广泛的搜索",
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)
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top_per_seed = st.slider(
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"每个种子的局部最优数 (top_per_seed)",
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min_value=1,
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max_value=5,
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value=1,
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step=1,
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help="后续迭代中，每个种子点邻域保留的局部最优数量",
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)
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st.markdown("**迭代步长与轮数**")
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use_custom_steps = st.checkbox(
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"自定义迭代步长",
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value=False,
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help="默认 mol ratio 步长 [10, 2, 1]（百分数），weight ratio 步长 [5, 2, 1]，共3轮。将某轮步长设为0可减少迭代轮数。",
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)
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if use_custom_steps:
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st.caption("**Mol ratio 步长 (%)**")
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col1, col2, col3 = st.columns(3)
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with col1:
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step1 = st.number_input(
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"第1轮 mol 步长",
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min_value=1, max_value=20, value=10,
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step=1,
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help="第1轮为全局粗搜索",
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key="mol_step1",
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)
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with col2:
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step2 = st.number_input(
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"第2轮 mol 步长",
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min_value=0, max_value=10, value=2,
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step=1,
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help="设为0则只进行1轮搜索",
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key="mol_step2",
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)
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with col3:
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step3 = st.number_input(
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"第3轮 mol 步长",
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min_value=0, max_value=5, value=1,
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step=1,
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help="设为0则只进行2轮搜索",
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key="mol_step3",
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)
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st.caption("**Weight ratio 步长**")
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col1, col2, col3 = st.columns(3)
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with col1:
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wr_step1 = st.number_input(
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"第1轮 WR 步长",
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min_value=1.0, max_value=10.0, value=5.0,
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step=1.0, format="%.1f",
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key="wr_step1",
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)
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with col2:
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wr_step2 = st.number_input(
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"第2轮 WR 步长",
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min_value=0.0, max_value=5.0, value=2.0,
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step=0.5, format="%.1f",
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key="wr_step2",
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)
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with col3:
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wr_step3 = st.number_input(
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"第3轮 WR 步长",
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min_value=0.0, max_value=2.0, value=1.0,
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step=0.5, format="%.1f",
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key="wr_step3",
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)
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if step2 == 0:
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step_sizes = [float(step1)]
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wr_step_sizes_val = [wr_step1]
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elif step3 == 0:
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step_sizes = [float(step1), float(step2)]
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wr_step_sizes_val = [wr_step1, wr_step2]
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else:
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step_sizes = [float(step1), float(step2), float(step3)]
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wr_step_sizes_val = [wr_step1, wr_step2, wr_step3]
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st.caption(f"📌 实际迭代轮数: {len(step_sizes)} 轮，mol步长: {step_sizes}，WR步长: {wr_step_sizes_val}")
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else:
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step_sizes = None
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wr_step_sizes_val = None
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st.markdown("**组分范围限制**")
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use_custom_ranges = st.checkbox(
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"自定义组分取值范围",
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value=False,
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help="限制各组分的取值范围（mol 比例加起来仍为 100%）",
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)
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if use_custom_ranges:
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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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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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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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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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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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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")
|
||
|
||
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")
|
||
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")
|
||
|
||
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")
|
||
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")
|
||
|
||
comp_ranges = {
|
||
"weight_ratio_min": weight_ratio_min,
|
||
"weight_ratio_max": weight_ratio_max,
|
||
"cationic_mol_min": cationic_mol_min,
|
||
"cationic_mol_max": cationic_mol_max,
|
||
"phospholipid_mol_min": phospholipid_mol_min,
|
||
"phospholipid_mol_max": phospholipid_mol_max,
|
||
"cholesterol_mol_min": cholesterol_mol_min,
|
||
"cholesterol_mol_max": cholesterol_mol_max,
|
||
"peg_mol_min": peg_mol_min,
|
||
"peg_mol_max": peg_mol_max,
|
||
}
|
||
|
||
min_sum = cationic_mol_min + phospholipid_mol_min + cholesterol_mol_min + peg_mol_min
|
||
max_sum = cationic_mol_max + phospholipid_mol_max + cholesterol_mol_max + peg_mol_max
|
||
if min_sum > 100.0 or max_sum < 100.0:
|
||
st.warning("⚠️ 当前范围设置可能无法生成有效配方（mol 比例需加起来为 100%）")
|
||
else:
|
||
comp_ranges = None
|
||
|
||
st.markdown("**评分/排序权重**")
|
||
use_custom_scoring = st.checkbox(
|
||
"自定义评分权重",
|
||
value=False,
|
||
help="默认仅按目标器官分布排序。开启后可自定义多目标加权评分，总分 = 各项score之和。",
|
||
)
|
||
|
||
if use_custom_scoring:
|
||
st.caption("**回归任务权重**")
|
||
|
||
sw_biodist = st.number_input(
|
||
"器官分布 (Biodistribution)",
|
||
min_value=0.00, max_value=10.00, value=0.30,
|
||
step=0.05, format="%.2f", key="sw_biodist",
|
||
help="score = biodist_value × weight",
|
||
)
|
||
sw_delivery = st.number_input(
|
||
"量化递送 (Quantified Delivery)",
|
||
min_value=0.00, max_value=10.00, value=0.25,
|
||
step=0.05, format="%.2f", key="sw_delivery",
|
||
help="score = normalize(delivery, route) × weight",
|
||
)
|
||
sw_size = st.number_input(
|
||
"粒径 (Size, 80-150nm)",
|
||
min_value=0.00, max_value=10.00, value=0.05,
|
||
step=0.05, format="%.2f", key="sw_size",
|
||
help="score = (1 if 60≤size≤150 else 0) × weight",
|
||
)
|
||
|
||
st.caption("**包封率 (EE) 分类权重**")
|
||
col1, col2, col3 = st.columns(3)
|
||
with col1:
|
||
sw_ee0 = st.number_input("<50% (低)", min_value=0.00, max_value=1.00, value=0.00, step=0.01, format="%.2f", key="sw_ee0")
|
||
with col2:
|
||
sw_ee1 = st.number_input("50-80% (中)", min_value=0.00, max_value=1.00, value=0.02, step=0.01, format="%.2f", key="sw_ee1")
|
||
with col3:
|
||
sw_ee2 = st.number_input(">80% (高)", min_value=0.00, max_value=1.00, value=0.08, step=0.01, format="%.2f", key="sw_ee2")
|
||
|
||
st.caption("**PDI 分类权重**")
|
||
col1, col2, col3, col4 = st.columns(4)
|
||
with col1:
|
||
sw_pdi0 = st.number_input("<0.2 (优)", min_value=0.00, max_value=1.00, value=0.08, step=0.01, format="%.2f", key="sw_pdi0")
|
||
with col2:
|
||
sw_pdi1 = st.number_input("0.2-0.3 (良)", min_value=0.00, max_value=1.00, value=0.02, step=0.01, format="%.2f", key="sw_pdi1")
|
||
with col3:
|
||
sw_pdi2 = st.number_input("0.3-0.4 (中)", min_value=0.00, max_value=1.00, value=0.00, step=0.01, format="%.2f", key="sw_pdi2")
|
||
with col4:
|
||
sw_pdi3 = st.number_input(">0.4 (差)", min_value=0.00, max_value=1.00, value=0.00, step=0.01, format="%.2f", key="sw_pdi3")
|
||
|
||
st.caption("**毒性分类权重**")
|
||
col1, col2 = st.columns(2)
|
||
with col1:
|
||
sw_toxic0 = st.number_input("无毒", min_value=0.00, max_value=1.00, value=0.20, step=0.05, format="%.2f", key="sw_toxic0")
|
||
with col2:
|
||
sw_toxic1 = st.number_input("有毒", min_value=0.00, max_value=1.00, value=0.00, step=0.05, format="%.2f", key="sw_toxic1")
|
||
|
||
scoring_weights = {
|
||
"biodist_weight": sw_biodist,
|
||
"delivery_weight": sw_delivery,
|
||
"size_weight": sw_size,
|
||
"ee_class_weights": [sw_ee0, sw_ee1, sw_ee2],
|
||
"pdi_class_weights": [sw_pdi0, sw_pdi1, sw_pdi2, sw_pdi3],
|
||
"toxic_class_weights": [sw_toxic0, sw_toxic1],
|
||
}
|
||
else:
|
||
scoring_weights = None # 使用默认值（仅按 biodist 排序）
|
||
|
||
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() or not selected_routes,
|
||
)
|
||
|
||
# ========== 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():
|
||
# 解析多条 SMILES（用逗号分隔）
|
||
smiles_list = [s.strip() for s in smiles_input.split(",") if s.strip()]
|
||
|
||
if not smiles_list:
|
||
st.error("❌ 请输入有效的 SMILES 字符串")
|
||
else:
|
||
is_multi_smiles = len(smiles_list) > 1
|
||
all_results = []
|
||
all_dfs = []
|
||
errors = []
|
||
|
||
# 进度条
|
||
progress_bar = st.progress(0)
|
||
status_text = st.empty()
|
||
|
||
for idx, smiles in enumerate(smiles_list):
|
||
status_text.text(f"🔄 正在优化 SMILES {idx + 1}/{len(smiles_list)}...")
|
||
progress_bar.progress((idx) / len(smiles_list))
|
||
|
||
try:
|
||
results = call_optimize_api(
|
||
smiles=smiles,
|
||
organ=selected_organ,
|
||
top_k=top_k,
|
||
num_seeds=num_seeds,
|
||
top_per_seed=top_per_seed,
|
||
step_sizes=step_sizes,
|
||
wr_step_sizes=wr_step_sizes_val,
|
||
comp_ranges=comp_ranges,
|
||
routes=selected_routes,
|
||
scoring_weights=scoring_weights,
|
||
)
|
||
all_results.append({"smiles": smiles, "results": results})
|
||
|
||
# 为多 SMILES 模式添加 SMILES 标签
|
||
smiles_label = smiles[:30] + "..." if len(smiles) > 30 else smiles
|
||
df = format_results_dataframe(results, smiles_label if is_multi_smiles else None)
|
||
all_dfs.append(df)
|
||
|
||
except httpx.HTTPStatusError as e:
|
||
try:
|
||
error_detail = e.response.json().get("detail", str(e))
|
||
except:
|
||
error_detail = str(e)
|
||
errors.append(f"SMILES {idx + 1}: {error_detail}")
|
||
except httpx.RequestError as e:
|
||
errors.append(f"SMILES {idx + 1}: API 连接失败 - {e}")
|
||
except Exception as e:
|
||
errors.append(f"SMILES {idx + 1}: {e}")
|
||
|
||
progress_bar.progress(1.0)
|
||
status_text.empty()
|
||
progress_bar.empty()
|
||
|
||
# 显示错误
|
||
for err in errors:
|
||
st.error(f"❌ {err}")
|
||
|
||
# 保存结果
|
||
if all_results:
|
||
st.session_state["results"] = all_results[0]["results"] if len(all_results) == 1 else all_results
|
||
st.session_state["results_df"] = pd.concat(all_dfs, ignore_index=True) if all_dfs else None
|
||
st.session_state["smiles_used"] = smiles_list
|
||
st.session_state["organ_used"] = selected_organ
|
||
st.session_state["is_multi_smiles"] = is_multi_smiles
|
||
st.success(f"✅ 优化完成！成功处理 {len(all_results)}/{len(smiles_list)} 条 SMILES")
|
||
|
||
# 显示结果
|
||
if st.session_state["results"] is not None and st.session_state["results_df"] is not None:
|
||
results = st.session_state["results"]
|
||
df = st.session_state["results_df"]
|
||
is_multi_smiles = st.session_state.get("is_multi_smiles", False)
|
||
|
||
# 结果概览
|
||
if is_multi_smiles:
|
||
# 多 SMILES 模式
|
||
col1, col2, col3 = st.columns(3)
|
||
|
||
with col1:
|
||
# 获取 target_organ（从第一个结果）
|
||
first_result = results[0]["results"] if isinstance(results, list) else results
|
||
target_organ = first_result["target_organ"]
|
||
st.metric(
|
||
"目标器官",
|
||
ORGAN_LABELS.get(target_organ, target_organ).split(" ")[0],
|
||
)
|
||
|
||
with col2:
|
||
st.metric(
|
||
"SMILES 数量",
|
||
len(results) if isinstance(results, list) else 1,
|
||
)
|
||
|
||
with col3:
|
||
st.metric(
|
||
"总配方数",
|
||
len(df),
|
||
)
|
||
else:
|
||
# 单 SMILES 模式
|
||
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(
|
||
"最优分布",
|
||
f"{best_score*100:.2f}%",
|
||
)
|
||
|
||
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:
|
||
smiles_used = st.session_state.get("smiles_used", "")
|
||
if isinstance(smiles_used, list):
|
||
smiles_used = ",".join(smiles_used)
|
||
|
||
csv_content = create_export_csv(
|
||
df,
|
||
smiles_used,
|
||
st.session_state.get("organ_used", ""),
|
||
)
|
||
|
||
# 获取 target_organ
|
||
if is_multi_smiles:
|
||
target_organ = results[0]["results"]["target_organ"] if isinstance(results, list) else results["target_organ"]
|
||
else:
|
||
target_organ = results["target_organ"]
|
||
|
||
st.download_button(
|
||
label="📥 导出 CSV",
|
||
data=csv_content,
|
||
file_name=f"lnp_optimization_{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()
|
||
|