ArticleFrontiers in pharmacology2018
Predicting Off-Target Binding Profiles With Confidence Using Conformal Prediction.
Article in Frontiers in pharmacology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.Chemical research in toxicology · 2025Review
- Preclinical side effect prediction through pathway engineering of protein interaction network models.CPT: pharmacometrics & systems pharmacology · 2024Article
- CPSign: conformal prediction for cheminformatics modeling.Journal of cheminformatics · 2024Article
- A Comparative Analysis on the Potential Anticancer Properties of Tetrahydrocannabinol, Cannabidiol, and Tetrahydrocannabivarin Compounds Through In Silico Approach.Asian Pacific journal of cancer prevention : APJCP · 2024Article
- Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-Based Featurization Method.Journal of chemical information and modeling · 2023Article
- Predicting protein network topology clusters from chemical structure using deep learning.Journal of cheminformatics · 2022Article
- Machine Learning Strategies When Transitioning between Biological Assays.Journal of chemical information and modeling · 2021Article
- Nanomedicine of tyrosine kinase inhibitors.Theranostics · 2021Review
- Relevance of physicochemical properties and functional pharmacology data to predict the clinical safety profile of direct oral anticoagulants.Pharmacology research & perspectives · 2020Article
- SciPipe: A workflow library for agile development of complex and dynamic bioinformatics pipelines.GigaScience · 2019Article
- Exploiting machine learning for end-to-end drug discovery and development.Nature materials · 2019Article
- Predicting Off-Target Binding Profiles With Confidence Using Conformal Prediction.Frontiers in pharmacology · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ligand-based models can be used in drug discovery to obtain an early indication of potential off-target interactions that could be linked to adverse effects. Another application is to combine such models into a panel, allowing to compare and search for compounds with similar profiles. Most contemporary methods and implementations however lack valid measures of confidence in their predictions, and only provide point predictions. We here describe a methodology that uses Conformal Prediction for predicting off-target interactions, with models trained on data from 31 targets in the ExCAPE-DB dataset selected for their utility in broad early hazard assessment. Chemicals were represented by the signature molecular descriptor and support vector machines were used as the underlying machine learning method. By using conformal prediction, the results from predictions come in the form of confidence
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