ArticleInternational journal of molecular sciences2020
Similarity-Based Methods and Machine Learning Approaches for Target Prediction in Early Drug Discovery: Performance and Scope.
Article in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Artificial Intelligence-Guided Phenotypic Drug Repurposing Against Streptococcus pneumoniae.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- ToxMamba: a novel method for toxic peptide prediction based on the fusion of pretrained embeddings and multi-scale state space models.Briefings in bioinformatics · 2026Article
- A Combined Chemoinformatics- and Machine Learning-Based Approach Identifies Chlormidazole as a Drug Repurposing Candidate against Aggressive Prostate Cancer.Journal of medicinal chemistry · 2026Article
- Potentials of Machine Learning in Predicting Key Features of Synthetic Antimicrobial Polymers.ACS polymers Au · 2026Article
- Integrated Computer-Aided Drug Design: Advances in GPCR Natural Ligand Discovery.Cell biochemistry and biophysics · 2026Review
- Heterogeneous Network With Multiview Path Aggregation: Drug-Target Interaction Prediction Study Design.JMIR medical informatics · 2025Article
- AI-Driven Drug Discovery: A Comprehensive Review.ACS omega · 2025Review
- Article
- Prediction of compound-target interaction using several artificial intelligence algorithms and comparison with a consensus-based strategy.Journal of cheminformatics · 2024Article
- Drug-target interaction prediction based on protein features, using wrapper feature selection.Scientific reports · 2023Article
- Maximizing the Performance of Similarity-Based Virtual Screening Methods by Generating Synergy from the Integration of 2D and 3D Approaches.International journal of molecular sciences · 2022Article
- A new ChEMBL dataset for the similarity-based target fishing engine FastTargetPred: Annotation of an exhaustive list of linear tetrapeptides.Data in brief · 2022Article
- Studying and mitigating the effects of data drifts on ML model performance at the example of chemical toxicity data.Scientific reports · 2022Article
- Discovery of Kinase and Carbonic Anhydrase Dual Inhibitors by Machine Learning Classification and Experiments.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Azepine-Indole Alkaloids FromFrontiers in neuroscience · 2022Article
- A survey on computational taste predictors.European food research and technology = Zeitschrift fur Lebensmittel-Untersuchung und -Forschung. A · 2022Review
- Turbo prediction: a new approach for bioactivity prediction.Journal of computer-aided molecular design · 2022Article
- BonMOLière: Small-Sized Libraries of Readily Purchasable Compounds, Optimized to Produce Genuine Hits in Biological Screens across the Protein Space.International journal of molecular sciences · 2021Article
- VirtualTaste: a web server for the prediction of organoleptic properties of chemical compounds.Nucleic acids research · 2021Article
- Cheminformatics in Natural Product-based Drug Discovery.Molecular informatics · 2020Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Computational methods for predicting the macromolecular targets of drugs and drug-like compounds have evolved as a key technology in drug discovery. However, the established validation protocols leave several key questions regarding the performance and scope of methods unaddressed. For example, prediction success rates are commonly reported as averages over all compounds of a test set and do not consider the structural relationship between the individual test compounds and the training instances. In order to obtain a better understanding of the value of ligand-based methods for target prediction, we benchmarked a similarity-based method and a random forest based machine learning approach (both employing 2D molecular fingerprints) under three testing scenarios: a standard testing scenario with external data, a standard time-split scenario, and a scenario that is designed to most closely resemble real-world conditions. In addition, we deconvoluted the results based on the distances of the individual test molecules from the training data. We found that, surprisingly, the similarity-based approach generally outperformed the machine learning approach in all testing scenarios, even in cases where queries were structurally clearly distinct from the instances in the training (or reference) data, and despite a much higher coverage of the known target space.
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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.