ReviewComputational and structural biotechnology journal2019
Current computational methods for predicting protein interactions of natural products.
Review in Computational and structural biotechnology journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Contrastive learning in both structure and function spaces improve drug-target interaction prediction.BMC bioinformatics · 2026Article
- African Natural Products Database (ANPDB): A resource for exploring the therapeutic potential of natural compounds from Africa.Nucleic acids research · 2026Article
- Target fishing: from "needle in haystack" to "precise guidance"--new technology, new strategy and new opportunity.Frontiers in pharmacology · 2025Review
- Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors.Computational and structural biotechnology journal · 2024Review
- ReverseDock: a web server for blind docking of a single ligand to multiple protein targets using AutoDock Vina.Frontiers in molecular biosciences · 2023Article
- Highly Accessible Computational Prediction andOxidative medicine and cellular longevity · 2023Article
- BC-11 is a covalent TMPRSS2 fragment inhibitor that impedes SARS-CoV-2 host cell entry.Archiv der Pharmazie · 2023Article
- Article
- How to Completely Squeeze a Fungus-Advanced Genome Mining Tools for Novel Bioactive Substances.Pharmaceutics · 2022Review
- Anti-Inflammatory Potential of Fucoidan for Atherosclerosis: In Silico and In Vitro Studies in THP-1 Cells.Molecules (Basel, Switzerland) · 2022Article
- Biotechnological Innovations from Ocean: Transpiring Role of Marine Drugs in Management of Chronic Disorders.Molecules (Basel, Switzerland) · 2022Review
- Natural product drug discovery in the artificial intelligence era.Chemical science · 2022Review
- Computer-Aided Drug Design of Natural Candidates for the Treatment of Non-Communicable Diseases.Evidence-based complementary and alternative medicine : eCAM · 2022Article
- Text Mining Protocol to Retrieve Significant Drug-Gene Interactions from PubMed Abstracts.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Chemogenomic Approaches for Revealing Drug Target Interactions in Drug Discovery.Current genomics · 2021Review
- Quantitative prediction model for affinity of drug-target interactions based on molecular vibrations and overall system of ligand-receptor.BMC bioinformatics · 2021Article
- Development and characterization of a DNA aptamer for MLL-AF9 expressing acute myeloid leukemia cells using whole cell-SELEX.Scientific reports · 2021Article
- Comprehensive Survey of Recent Drug Discovery Using Deep Learning.International journal of molecular sciences · 2021Review
- Pathway-based protein-protein association network to explore mechanism of α-glucosidase inhibitors from Scutellaria baicalensis Georgi against type 2 diabetes.IET systems biology · 2021Article
- StreptomeDB 3.0: an updated compendium of streptomycetes natural products.Nucleic acids research · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Natural products (NPs) are an indispensable source of drugs and they have a better coverage of the pharmacological space than synthetic compounds, owing to their high structural diversity. The prediction of their interaction profiles with druggable protein targets remains a major challenge in modern drug discovery. Experimental (off-)target predictions of NPs are cost- and time-consuming, whereas computational methods, on the other hand, are much faster and cheaper. As a result, computational predictions are preferentially used in the first instance for NP profiling, prior to experimental validations. This review covers recent advances in computational approaches which have been developed to aid the annotation of unknown drug-target interactions (DTIs), by focusing on three broad classes, namely: ligand-based, target-based, and target-ligand-based (hybrid) approaches. Computational DTI prediction methods have the potential to significantly advance the discovery and development of novel selective drugs exhibiting minimal side effects. We highlight some inherent caveats of these methods which must be overcome to enable them to realize their full potential, and a future outlook is given.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.