Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Molecules

DOI: 10.3390/molecules30081683

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

Institutions: Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Ministry of Agriculture and Rural Affairs, Kashi University

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,258

Funder: National Natural Science Foundation of China