ArticleInternational journal of molecular sciences2021
Target Prediction Model for Natural Products Using Transfer Learning.
Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- KAN-PROSPECT: a Kolmogorov-Arnold Networks-integrated framework for predicting the effects and adverse reactions of natural products via transfer learning.Scientific reports · 2026Article
- Natural Products in Epilepsy Treatment: From Traditional Medicine Towards Computational Drug Discovery.Current issues in molecular biology · 2026Review
- Rethinking Nature's Pharmacy: AI Era and Natural Product Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Formulation Strategies for Immunomodulatory Natural Products in 3D Tumor Spheroids and Organoids: Current Challenges and Emerging Solutions.Pharmaceutics · 2025Review
- Machine Learning for Multi-Target Drug Discovery: Challenges and Opportunities in Systems Pharmacology.Pharmaceutics · 2025Review
- Natural product databases for drug discovery: Features and applications.Pharmaceutical science advances · 2024Review
- Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products.Natural product reports · 2024Review
- A Multi-Label Learning Framework for Predicting Chemical Classes and Biological Activities of Natural Products from Biosynthetic Gene Clusters.Journal of chemical ecology · 2023Article
- How to Completely Squeeze a Fungus-Advanced Genome Mining Tools for Novel Bioactive Substances.Pharmaceutics · 2022Review
- Computer-Aided Drug Design (CADD) to De-Orphanize Marine Molecules: Finding Potential Therapeutic Agents for Neurodegenerative and Cardiovascular Diseases.Marine drugs · 2022Article
- Inhibition Ability of Natural Compounds on Receptor-Binding Domain of SARS-CoV2: An In Silico Approach.Pharmaceuticals (Basel, Switzerland) · 2021Article
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Authors and funding
5 authors.
Funding
Abstract
A large proportion of lead compounds are derived from natural products. However, most natural products have not been fully tested for their targets. To help resolve this problem, a model using transfer learning was built to predict targets for natural products. The model was pre-trained on a processed ChEMBL dataset and then fine-tuned on a natural product dataset. Benefitting from transfer learning and the data balancing technique, the model achieved a highly promising area under the receiver operating characteristic curve (AUROC) score of 0.910, with limited task-related training samples. Since the embedding distribution difference is reduced, embedding space analysis demonstrates that the model's outputs of natural products are reliable. Case studies have proved our model's performance in drug datasets. The fine-tuned model can successfully output all the targets of 62 drugs. Compared with a previous study, our model achieved better results in terms of both AUROC validation and its success rate for obtaining active targets among the top ones. The target prediction model using transfer learning can be applied in the field of natural product-based drug discovery and has the potential to find more lead compounds or to assist researchers in drug repurposing.
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Registered trials
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