DerivaPredict: A User-Friendly Tool for Predicting and Evaluating Active Derivatives of Natural Products

文献类型： 外文期刊

第一作者： Song, Yu

作者： Song, Yu;Chu, Ganghui;Song, Yu;Zhang, Meng;Chang, Sihao;Ji, Hongchao

作者机构：

关键词： natural product derivatives; in silico molecular design; software engineering

期刊名称：MOLECULES （ 影响因子：4.6； 五年影响因子：5.0 ）

ISSN：

年卷期： 2025 年 30 卷 8 期

页码：

收录情况： SCI

摘要： While natural products and derivatives have been crucial in drug discovery, the current databases are limited to known compounds. There is a need for tools that can automatically generate and assess novel derivatives of natural products to enhance early-stage drug discovery. We present DerivaPredict (v1.0), a user-friendly tool that generates novel natural product derivatives through chemical and metabolic transformations. It predicts binding affinities using pretrained deep learning models and assesses drug-likeness via ADMET profiling. DerivaPredict is freely accessible with a source code on GitHub.

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