抗SARS-CoV-2基准数据集
基于ChEMBL抗SARS-CoV-2生物活性数据构建的基准数据集，用于机器学习预测抗SARS-CoV-2活性。
基本信息
资源简介
该数据集从ChEMBL数据库收集抗SARS-CoV-2生物活性数据，构建基准数据集，用于通过机器学习方法预测抗SARS-CoV-2活性，以发现新的抗SARS-CoV-2化合物和药用植物。数据模态为表格，包含化合物结构和活性标签。
下载信息
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使用方式
数据集获取
git clone https://github.com/daishaoxing/anti-SARS-CoV-2.git
curl -L -o repo.zip https://github.com/daishaoxing/anti-SARS-CoV-2/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录（使用方式）
anti-SARS-CoV-2
The code and associated dataset for the study of “In silico identification of anti-SARS-CoV-2 medicinal plants using cheminformatics and machine learning”
Abstract:
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative pathogen of COVID-19, is spreading rapidly and has caused hundreds of millions of infections and millions of deaths worldwide. Due to the lack of specific vaccines and effective treatments for COVID-19, there is an urgent need to identify effective drugs. Traditional Chinese medicine (TCM) is a valuable resource for identifying novel anti-SARS-CoV-2 drugs based on the important contribution of TCM and its potential benefits in COVID-19 treatment. Herein, we aimed to discover novel an-ti-SARS-CoV-2 compounds and medicinal plants from TCM by establishing a prediction method of anti-SARS-CoV-2 activity using machine learning methods. We firstly constructed a benchmark dataset from anti-SARS-CoV-2 bioactivity data collected from the ChEMBL database. Then, we established random forest (RF) and support vector machine (SVM) models that both achieved satisfactorily predictive performance with AUC values of 0.90. By using this method, a total of 1013 active anti-SARS-CoV-2 compounds were predicted from the TCMSP database. Among these compounds, six compounds with highly potent activity were confirmed in the anti-SARS-CoV-2 experiments. The molecular fingerprint similarity analysis revealed that only 24 of the 1013 compounds have high similarity to the FDA-approved antiviral drugs, indicating that most of the compounds were structurally novel. Based on the predicted anti-SARS-CoV-2 compounds, we identified 74 anti-SARS-CoV-2 medicinal plants through enrichment analysis. These identified plants are widely distributed in 68 genera and 43 families. In summary, this study provided several medicinal plants with potential anti-SARS-CoV-2 activity, which offer an attractive starting point and a broader scope to mine for potentially novel anti-SARS-CoV-2 drugs.
Introduction
This repository contains an anti-SARS-CoV-2 compound predictor constructed based on machine learning methods for screening novel anti-SARS-CoV-2 drugs from large compound database.
Requirement
the predictor is a program developing with Python. Pybel, a python wrapper of Openbabel, was used to deal with compounds and generate molecular fingerprints fo
数据加载示例（表格/文本类）
import pandas as pd, glob, os
files = (glob.glob(os.path.join(path, "**", "*.csv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tsv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.xlsx"), recursive=True))
print("数据文件:", files)
df = pd.read_csv(files[0])
print(df.shape); print(df.columns.tolist()); print(df.head(3))
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