ArticleNucleic acids research2019
Machine learning-based chemical binding similarity using evolutionary relationships of target genes.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- A computational framework for proteome-wide target profiling of natural products: mechanistic and therapeutic insights into ginsenosides.Briefings in bioinformatics · 2026Article
- CACHE Challenge #3: Targeting the Nsp3 Macrodomain of SARS-CoV-2.Journal of chemical information and modeling · 2026Article
- 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
- Novel target identification towards drug repurposing based on biological activity profiles.PloS one · 2025Article
- New Small-Molecule SERCA Inhibitors Enhance Treatment Efficacy in Lenvatinib-Resistant Papillary Thyroid Cancer.International journal of molecular sciences · 2024Article
- Iterative machine learning-based chemical similarity search to identify novel chemical inhibitors.Journal of cheminformatics · 2023Article
- Ecdysteroids from the Korean Endemic SpeciesACS omega · 2023Article
- Ginsenoside Rd ameliorates muscle wasting by suppressing the signal transducer and activator of transcription 3 pathway.Journal of cachexia, sarcopenia and muscle · 2022Article
- Drug Discovery Using Evolutionary Similarities in Chemical Binding to Inhibit Patient-Derived Hepatocellular Carcinoma.International journal of molecular sciences · 2022Article
- Bioactivity assessment of natural compounds using machine learning models trained on target similarity between drugs.PLoS computational biology · 2022Article
- Identification of Tyrosinase Inhibitors and Their Structure-Activity Relationships via Evolutionary Chemical Binding Similarity and Structure-Based Methods.Molecules (Basel, Switzerland) · 2021Article
- Systems Pharmacology-Based Identification of Mechanisms of Action of Bolbostemma paniculatum for the Treatment of Hepatocellular Carcinoma.Medical science monitor : international medical journal of experimental and clinical research · 2021Article
- Evolutionary chemical binding similarity approach integrated with 3D-QSAR method for effective virtual screening.BMC bioinformatics · 2020Article
- Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches.Biotechnology and bioprocess engineering : BBE · 2020Review
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Chemical similarity searching is a basic research tool that can be used to find small molecules which are similar in shape to known active molecules. Despite its popularity, the retrieval of local molecular features that are critical to functional activity related to target binding often fails. To overcome this limitation, we developed a novel machine learning-based chemical binding similarity score by using various evolutionary relationships of binding targets. The chemical similarity was defined by the probability of chemical compounds binding to identical targets. Comprehensive and heterogeneous multiple target-binding chemical data were integrated into a paired data format and processed using multiple classification similarity-learning models with various levels of target evolutionary information. Encoding evolutionary information to chemical compounds through their binding targets substantially expanded available chemical-target interaction data and significantly improved model performance. The output probability of our integrated model, referred to as ensemble evolutionary chemical binding similarity (ensECBS), was effective for finding hidden chemical relationships. The developed method can serve as a novel chemical similarity tool that uses evolutionarily conserved target binding information.
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