Data, Models, and Python Code For: Machine learning models and performance dependency on 2D chemical descriptor space for retention time prediction of pharmaceuticals

Training data, models, and python code for the manuscript: Machine learning models and performance dependency on 2D chemical descriptor space for retention time prediction of pharmaceuticals. MOE descriptors of the METLIN SMRT dataset (original by Domingo-Almenara et. al. with RTs and structures available at: https://figshare.com/ndownloader/files/18130628), training scripts (python), UMAP, GMM, and SVR models with training splits results are within the SMRT_exp.zip file. CSVs for feature importance for SVR models are standalone files.本数据集包含用于撰写研究手稿《用于药物保留时间预测的机器学习模型及其性能与二维化学描述符空间的依赖性》的训练数据、模型与Python代码。 METLIN SMRT数据集的MOE描述符（Molecular Operating Environment descriptors，由Domingo-Almenara等人原创，其附带的保留时间（retention times, RTs）与分子结构可通过链接https://figshare.com/ndownloader/files/18130628下载）、Python训练脚本、均匀流形近似与投影（Uniform Manifold Approximation and Projection, UMAP）模型、高斯混合模型（Gaussian Mixture Model, GMM）与支持向量回归（Support Vector Regression, SVR）模型及其训练集划分结果均存储于SMRT_exp.zip压缩包内。 支持向量回归模型的特征重要性逗号分隔值（Comma-Separated Values, CSV）文件均为独立文件。