Bioactivity deep learning for structure-free compound-protein interaction

CPI2M data for "Bioactivity deep learning for structure-free compound-protein interaction".

CPI2M_main_Ki.csv: Bioactivity data with pKi activity type. Used for model training and internal validation.

CPI2M_main_Kd.csv: Bioactivity data with pKd activity type. Used for model training and internal validation.

CPI2M_main_EC50.csv: Bioactivity data with pEC50 activity type. Used for model training and internal validation.

CPI2M_main_IC50.csv: Bioactivity data with pIC50 activity type. Used for model training and internal validation.

CPI2M_few_Ki.csv: Bioactivity data with pKi activity type. Used for external validation.

CPI2M_few_Kd.csv: Bioactivity data with pKd activity type. Used for external validation.

CPI2M_few_EC50.csv: Bioactivity data with pEC50 activity type. Used for external validation.

CPI2M_few_IC50.csv: Bioactivity data with pIC50 activity type. Used for external validation.

potency.csv: BIoactivity data with pPotency activity type. Not used currently but can be potentially adopted as classification data for customized use.

percentage.csv: BIoactivity data with Percentage Inhibition activity type. Not used currently but can be potentially adopted as classification data for customized use.

Protein_pretrained_feat.zip: pre-calculated protein feature files with UniProt ID naming. Should be unzipped before start model training with CPI2M data.

For each .csv data, columns include "smiles" (ligand SMILES), "exp_mean" (nM bioactivity), "y" (neg.log nM, final label), "cliff_mol" (whether activity cliff or not), "split" (splitting label by activity cliff), "Uniprot_id" (UniProt ID for protein), "Sequence" (wildtype sequence for protein), and "type_id" (bioactivity type token, pKi =0, pKd=1, pEC50=2, pIC50=3).

Please find the project code at https://github.com/gu-yaowen/GGAP-CPI

用于“无结构化合物-蛋白质相互作用的生物活性深度学习”研究的CPI2M数据集。

CPI2M_main_Ki.csv：包含pKi活性类型的生物活性数据，用于模型训练与内部验证。
CPI2M_main_Kd.csv：包含pKd活性类型的生物活性数据，用于模型训练与内部验证。
CPI2M_main_EC50.csv：包含pEC50活性类型的生物活性数据，用于模型训练与内部验证。
CPI2M_main_IC50.csv：包含pIC50活性类型的生物活性数据，用于模型训练与内部验证。
CPI2M_few_Ki.csv：包含pKi活性类型的生物活性数据，用于外部验证。
CPI2M_few_Kd.csv：包含pKd活性类型的生物活性数据，用于外部验证。
CPI2M_few_EC50.csv：包含pEC50活性类型的生物活性数据，用于外部验证。
CPI2M_few_IC50.csv：包含pIC50活性类型的生物活性数据，用于外部验证。
potency.csv：包含pPotency活性类型的生物活性数据，当前暂未使用，但可经适配后用作自定义分类任务的训练数据。
percentage.csv：包含百分比抑制率（Percentage Inhibition）活性类型的生物活性数据，当前暂未使用，但可经适配后用作自定义分类任务的训练数据。

Protein_pretrained_feat.zip：预计算得到的蛋白质特征文件，采用UniProt ID命名规范。使用该数据集开展模型训练前，需先对其进行解压。

对于每个.csv数据文件，其列字段包括："smiles"（配体SMILES字符串）、"exp_mean"（纳摩尔级生物活性均值）、"y"（负对数纳摩尔值，即最终标签）、"cliff_mol"（是否为活性悬崖分子）、"split"（基于活性悬崖的划分标签）、"Uniprot_id"（蛋白质的UniProt ID）、"Sequence"（蛋白质野生型序列），以及"type_id"（生物活性类型Token，其中pKi对应0、pKd对应1、pEC50对应2、pIC50对应3）。

项目代码请访问：https://github.com/gu-yaowen/GGAP-CPI