DerivaPredict：一种用于预测和评估天然产物活性衍生物的用户友好型工具。
DerivaPredict: A User-Friendly Tool for Predicting and Evaluating Active Derivatives of Natural Products.
作者信息
Song Yu, Zhang Meng, Chang Sihao, Chu Ganghui, Ji Hongchao
机构信息
Laboratory of Xinjiang Native Medicinal and Edible Plant Resource Chemistry, College of Chemistry and Environmental Science, Kashi University, Kashi 844006, China.
Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China.
出版信息
Molecules. 2025 Apr 9;30(8):1683. doi: 10.3390/molecules30081683.
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.
虽然天然产物及其衍生物在药物发现中至关重要，但目前的数据库仅限于已知化合物。需要能够自动生成和评估天然产物新型衍生物的工具，以加强早期药物发现。我们展示了DerivaPredict（v1.0），这是一个用户友好的工具，可通过化学和代谢转化生成新型天然产物衍生物。它使用预训练的深度学习模型预测结合亲和力，并通过ADMET分析评估药物相似性。DerivaPredict可在GitHub上免费获取其源代码。
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