ArticlePLoS computational biology2022
Bioactivity assessment of natural compounds using machine learning models trained on target similarity between drugs.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 43 citations in OpenAlex.
- Drug-microbiome-host interactions: antimicrobial effects of non-antibiotic compounds.Nature reviews. Gastroenterology & hepatology · 2026Review
- Machine Learning-Driven Discovery of Novel HER2 Inhibitors Through Integrated Virtual Screening and Molecular Dynamics Simulations.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Combating Antibacterial Resistance: The Integrative Role of Artificial Intelligence in Bio-Based Product Development.Antibiotics (Basel, Switzerland) · 2026Review
- Artificial intelligence in functional food innovation: Bioactive enhancement and formulation optimization: A quasi-systematic review.Food chemistry: X · 2026Review
- Unveiling potent xanthine oxidase inhibitors in two Balanophora spp. using machine learning-based virtual screening and molecular docking approach.Scientific reports · 2025Article
- Decoding GuaB: Machine Learning-Powered Discovery of Enzyme Inhibitors Against the SuperbugPharmaceuticals (Basel, Switzerland) · 2025Article
- Industrial and agricultural chemicals exhibit antimicrobial activity against human gut bacteria in vitro.Nature microbiology · 2025Article
- Developing a quantitative structure-property relationships (QSPR) model using Caco-2 cell bioavailability indicators (BA) to predict the BA of phytochemicals.Journal of the science of food and agriculture · 2025Article
- Smartphones as Catalysts for Synergistic Nutrition: A New Era in Bioactive Detection, Personalization, and Food System Intelligence.Food science & nutrition · 2025Review
- Quantum-inspired computational drug design for phytopharmaceuticals: a herbal holography analysis.Journal of molecular modeling · 2025Review
- Discovery of SARS-CoV-2 Nsp14-Methyltransferase (MTase) Inhibitors by Harnessing Scaffold-Centric Exploration of the Ultra Large Chemical Space.ACS pharmacology & translational science · 2025Article
- Mild Blast Exposure Dysregulates Metabolic Pathways and Correlation Networking as Evident from LC-MS-Based Plasma Profiling.Molecular neurobiology · 2025Article
- Beyond Tanimoto: a learned bioactivity similarity index enhances ligand discovery.Frontiers in bioinformatics · 2025Article
- A perspective on the mechanisms of herbal medicine for cognitive impairment.Frontiers in neurology · 2025Article
- Attention-based approach to predict drug-target interactions across seven target superfamilies.Bioinformatics (Oxford, England) · 2024Article
- Hybrid deep learning technique for COX-2 inhibition bioactivity detection against breast cancer disease.Biomedical engineering letters · 2024Article
- BATMAN-TCM 2.0: an enhanced integrative database for known and predicted interactions between traditional Chinese medicine ingredients and target proteins.Nucleic acids research · 2024Article
- Article
- Natural Phenolic Compounds with Antithrombotic and Antiplatelet Effects: A Drug-likeness Approach.Current medicinal chemistry · 2024Review
- Adapting physiologically-based pharmacokinetic models for machine learning applications.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
Abstract
Natural compounds constitute a rich resource of potential small molecule therapeutics. While experimental access to this resource is limited due to its vast diversity and difficulties in systematic purification, computational assessment of structural similarity with known therapeutic molecules offers a scalable approach. Here, we assessed functional similarity between natural compounds and approved drugs by combining multiple chemical similarity metrics and physicochemical properties using a machine-learning approach. We computed pairwise similarities between 1410 drugs for training classification models and used the drugs shared protein targets as class labels. The best performing models were random forest which gave an average area under the ROC of 0.9, Matthews correlation coefficient of 0.35, and F1 score of 0.33, suggesting that it captured the structure-activity relation well. The models were then used to predict protein targets of circa 11k natural compounds by comparing them with the drugs. This revealed therapeutic potential of several natural compounds, including those with support from previously published sources as well as those hitherto unexplored. We experimentally validated one of the predicted pair's activities, viz., Cox-1 inhibition by 5-methoxysalicylic acid, a molecule commonly found in tea, herbs and spices. In contrast, another natural compound, 4-isopropylbenzoic acid, with the highest similarity score when considering most weighted similarity metric but not picked by our models, did not inhibit Cox-1. Our results demonstrate the utility of a machine-learning approach combining multiple chemical features for uncovering protein binding potential of natural compounds.
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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.