139 lines
4.1 KiB
Markdown
139 lines
4.1 KiB
Markdown
## MolE 广谱抗菌预测 API
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测试案例： example_usage.py
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## 功能特性
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1. **高性能并行处理** - 支持多进程并行计算，显著提高大批量分子预测速度
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2. **多种使用方式** - 提供Python API、命令行工具和Web服务三种使用方式
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3. **模块化设计** - 易于集成到其他项目中
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4. **灵活配置** - 支持自定义模型路径、阈值等参数
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## 安装
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```bash
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pip install -e .
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```
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## 使用方式
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### 1. Python API
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```python
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from broad_spectrum_parallel import predict_smiles, MoleculeInput
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# 预测单个或多个SMILES
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results = predict_smiles(["CCO", "CCN"], ["ethanol", "ethylamine"])
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for result in results:
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print(f"{result.chem_id}: 广谱={result.broad_spectrum}, 抑制数={result.ginhib_total}")
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```
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### 2. 命令行工具
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```bash
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# 基本用法
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predict_antimicrobial input.tsv output.tsv --smiles_input --smiles_colname smiles --chemid_colname chem_id
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# 聚合预测结果
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predict_antimicrobial input.tsv output.tsv --smiles_input --aggregate_scores
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```
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### 3. Web API服务
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```bash
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# 启动服务
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uvicorn broad_spectrum_parallel.api:app --host 0.0.0.0 --port 8000
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```
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然后可以通过POST请求访问`http://localhost:8000/predict`端点：
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```bash
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curl -X POST "http://localhost:8000/predict" \
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-H "Content-Type: application/json" \
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-d '{"smiles": ["CCO", "CCN"]}'
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```
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## 结果解读
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从运行结果可以看到，每个化合物返回8个关键指标：
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1. 抗菌潜力分数（对数尺度）：
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apscore_total: -11.758 - 总体抗菌分数
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apscore_gnegative: -11.648 - 革兰阴性菌抗菌分数
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apscore_gpositive: -11.848 - 革兰阳性菌抗菌分数
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2. 抑制菌株统计：
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ginhib_total: 0 - 总抑制菌株数
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ginhib_gnegative: 0 - 抑制的革兰阴性菌株数
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ginhib_gpositive: 0 - 抑制的革兰阳性菌株数
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3. 广谱判定：
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broad_spectrum: 0 - 是否广谱抗菌（需抑制≥10个菌株）
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🧪 结果解释示例
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以乙醇(CCO)为例：
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抗菌分数很低 (-11.758)：表明预测的抗菌活性很弱
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无菌株抑制 (0)：在设定阈值下不能有效抑制任何测试菌株
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非广谱抗菌 (0)：不满足广谱抗菌的最低标准
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这个结果符合预期，因为乙醇虽有杀菌作用，但在药物发现的标准下不被认为是有效的抗菌候选化合物。
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## 可以运行的菌株信息
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```shell
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(mole) root@DESK4090:/srv/project/mole_antimicrobial_potential/broad_spectrum_parallel# micromamba run -n mole python -c "
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> import pandas as pd
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> import numpy as np
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>
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> # 加载菌株筛选数据
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> maier_screen = pd.read_csv('data/01.prepare_training_data/maier_screening_results.tsv.gz', sep='\t', index_col=0)
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> print(f'总菌株数量: {len(maier_screen.columns)}')
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> print(f'总化合物数量: {len(maier_screen.index)}')
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> print(f'菌株列表前10个:')
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> for i, strain in enumerate(maier_screen.columns[:10]):
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> print(f'{i+1}. {strain}')
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>
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> # 加载革兰染色信息
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> gram_info = pd.read_excel('raw_data/maier_microbiome/strain_info_SF2.xlsx',
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> skiprows=[0, 1, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54],
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> index_col='NT data base')
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> print(f'\n革兰染色信息:')
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> print(gram_info['Gram stain'].value_counts())
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> "
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总菌株数量: 40
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总化合物数量: 1197
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菌株列表前10个:
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1. Akkermansia muciniphila (NT5021)
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2. Bacteroides caccae (NT5050)
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3. Bacteroides fragilis (ET) (NT5033)
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4. Bacteroides fragilis (NT) (NT5003)
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5. Bacteroides ovatus (NT5054)
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6. Bacteroides thetaiotaomicron (NT5004)
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7. Bacteroides uniformis (NT5002)
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8. Bacteroides vulgatus (NT5001)
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9. Bacteroides xylanisolvens (NT5064)
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10. Bifidobacterium adolescentis (NT5022)
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/root/micromamba/envs/mole/lib/python3.10/site-packages/openpyxl/worksheet/_reader.py:329: UserWarning: Unknown extension is not supported and will be removed
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warn(msg)
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革兰染色信息:
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Gram stain
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positive 22
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negative 18
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Name: count, dtype: int64
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```
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## 权重下载
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mole
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https://www.alipan.com/s/DNuDo8iEn89
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提取码: mh90
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下载完成放到：pretrained_model/model_ginconcat_btwin_100k_d8000_l0.0001
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## 原始论文与github仓库
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https://www.nature.com/articles/s41467-025-58804-4
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https://github.com/rolayoalarcon/mole_antimicrobial_potential |