DerivaPredict: A User-Friendly Tool for Predicting and Evaluating Active Derivatives of Natural Products
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.
Authors
- Ganghui Chu
- Hongchao Ji (ORCID: https://orcid.org/0000-0002-7364-0741)
- Yu Song (ORCID: https://orcid.org/0000-0003-4836-3954)
- Meng Zhang (ORCID: https://orcid.org/0000-0001-5630-321X)
- Sihao Chang
Institutions
- Agricultural Genomics Institute at Shenzhen (CN)
- Chinese Academy of Agricultural Sciences (CN)
- Ministry of Agriculture and Rural Affairs (CN)
Publication Details
- Journal
- Molecules
- Published
- 2025-04-09
- DOI
- https://doi.org/10.3390/molecules30081683
- Citations
- 3
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 4.51
Funders
- National Natural Science Foundation of China