DerivaPredict: A User-Friendly Tool for Predicting and Evaluating Active Derivatives of Natural Products
文献类型： 外文期刊
作者： Song, Yu 1 ; Zhang, Meng 2 ; Chang, Sihao 2 ; Chu, Ganghui 1 ; Ji, Hongchao 2 ;
作者机构： 1.Kashi Univ, Coll Chem & Environm Sci, Lab Xinjiang Native Med & Edible Plant Resource Ch, Kashi 844006, Peoples R China
2.Chinese Acad Agr Sci, Agr Genom Inst Shenzhen, Shenzhen Branch, Guangdong Lab Lingnan Modern Agr,Genome Anal Lab,M, Shenzhen 518120, Peoples R China
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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