ArticleScientific reports2019
Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed, 54 citations in OpenAlex.
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- Exploring Scoring Function Space: Developing Computational Models for Drug Discovery.Current medicinal chemistry · 2024Review
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- Prediction of protein-ligand binding affinity with deep learning.Computational and structural biotechnology journal · 2023Review
- In Vitro and In Silico Studies for the Identification of Potent Metabolites of Some High-Altitude Medicinal Plants from Nepal Inhibiting SARS-CoV-2 Spike Protein.Molecules (Basel, Switzerland) · 2022Article
- In silico prediction suggests inhibitory effect of halogenated boroxine on human catalase and carbonic anhydrase.Journal, genetic engineering & biotechnology · 2022Article
- A machine learning model for classifying G-protein-coupled receptors as agonists or antagonists.BMC bioinformatics · 2022Article
- Toward the Identification of Natural Antiviral Drug Candidates against Merkel Cell Polyomavirus: Computational Drug Design Approaches.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Metabolization and sequestration of plant specialized metabolites in insect herbivores: Current and emerging approaches.Frontiers in physiology · 2022Review
- SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction.International journal of molecular sciences · 2021Article
- A review on compound-protein interaction prediction methods: Data, format, representation and model.Computational and structural biotechnology journal · 2021Review
- A review on machine learning approaches and trends in drug discovery.Computational and structural biotechnology journal · 2021Review
- Identifying GPCR-drug interaction based on wordbook learning from sequences.BMC bioinformatics · 2020Article
- BOW-GBDT: A GBDT Classifier Combining With Artificial Neural Network for Identifying GPCR-Drug Interaction Based on Wordbook Learning From Sequences.Frontiers in cell and developmental biology · 2020Article
- Improvement of Prediction Performance With Conjoint Molecular Fingerprint in Deep Learning.Frontiers in pharmacology · 2020Article
- Development of simultaneous interaction prediction approach (SiPA) for the expansion of interaction network of traditional Chinese medicine.Chinese medicine · 2020Article
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Authors and funding
11 authors at 9 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying potential protein-ligand interactions is central to the field of drug discovery as it facilitates the identification of potential novel drug leads, contributes to advancement from hits to leads, predicts potential off-target explanations for side effects of approved drugs or candidates, as well as de-orphans phenotypic hits. For the rapid identification of protein-ligand interactions, we here present a novel chemogenomics algorithm for the prediction of protein-ligand interactions using a new machine learning approach and novel class of descriptor. The algorithm applies Bayesian Additive Regression Trees (BART) on a newly proposed proteochemical space, termed the bow-pharmacological space. The space spans three distinctive sub-spaces that cover the protein space, the ligand space, and the interaction space. Thereby, the model extends the scope of classical target prediction or chemogenomic modelling that relies on one or two of these subspaces. Our model demonstrated excellent prediction power, reaching accuracies of up to 94.5-98.4% when evaluated on four human target datasets constituting enzymes, nuclear receptors, ion channels, and G-protein-coupled receptors . BART provided a reliable probabilistic description of the likelihood of interaction between proteins and ligands, which can be used in the prioritization of assays to be performed in both discovery and vigilance phases of small molecule development.
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