ReviewBriefings in bioinformatics2020
Validation strategies for target prediction methods.
Review in Briefings in bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
28 citing papers in PubMed.
- ProVenTL: a transfer-learning framework for predicting peptide-protein interactions derived from snake venom for cancer therapeutics.Journal of computer-aided molecular design · 2026Article
- Computational identification and validation of non-coding rna biomarkers in gastrointestinal cancer.Functional & integrative genomics · 2025Review
- Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.Chemical research in toxicology · 2025Review
- Inactive-enriched machine-learning models exploiting patent data improve structure-based virtual screening for PDL1 dimerizers.Journal of advanced research · 2025Article
- The Application of a Random Forest Classifier to ToF-SIMS Imaging Data.Journal of the American Society for Mass Spectrometry · 2024Article
- Synthesis of new non-natural L-glycosidic flavonoid derivatives and their evaluation as inhibitors of Trypanosoma cruzi ecto-nucleoside triphosphate diphosphohydrolase 1 (TcNTPDase1).Purinergic signalling · 2024Article
- Step Forward Cross Validation for Bioactivity Prediction: Out of Distribution Validation in Drug Discovery.bioRxiv : the preprint server for biology · 2024Article
- Clinical Applications of Machine Learning.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2024Review
- Quantifying the Hardness of Bioactivity Prediction Tasks for Transfer Learning.Journal of chemical information and modeling · 2024Article
- Testing the predictive power of reverse screening to infer drug targets, with the help of machine learning.Communications chemistry · 2024Article
- Tackling assay interference associated with small molecules.Nature reviews. Chemistry · 2024Review
- Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery.Life (Basel, Switzerland) · 2024Review
- Harnessing the power of machine learning for crop improvement and sustainable production.Frontiers in plant science · 2024Review
- Approaches for Benchmarking Single-Cell Gene Regulatory Network Methods.Bioinformatics and biology insights · 2024Review
- Drug repurposing and prediction of multiple interaction types via graph embedding.BMC bioinformatics · 2023Article
- BETA: a comprehensive benchmark for computational drug-target prediction.Briefings in bioinformatics · 2022Article
- Insight into the Structure-Odor Relationship of Molecules: A Computational Study Based on Deep Learning.Foods (Basel, Switzerland) · 2022Article
- Evaluation guidelines for machine learning tools in the chemical sciences.Nature reviews. Chemistry · 2022Review
- Novel drug-target interactions via link prediction and network embedding.BMC bioinformatics · 2022Article
- LigTMap: ligand and structure-based target identification and activity prediction for small molecular compounds.Journal of cheminformatics · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational methods for target prediction, based on molecular similarity and network-based approaches, machine learning, docking and others, have evolved as valuable and powerful tools to aid the challenging task of mode of action identification for bioactive small molecules such as drugs and drug-like compounds. Critical to discerning the scope and limitations of a target prediction method is understanding how its performance was evaluated and reported. Ideally, large-scale prospective experiments are conducted to validate the performance of a model; however, this expensive and time-consuming endeavor is often not feasible. Therefore, to estimate the predictive power of a method, statistical validation based on retrospective knowledge is commonly used. There are multiple statistical validation techniques that vary in rigor. In this review we discuss the validation strategies employed, highlighting the usefulness and constraints of the validation schemes and metrics that are employed to measure and describe performance. We address the limitations of measuring only generalized performance, given that the underlying bioactivity and structural data are biased towards certain small-molecule scaffolds and target families, and suggest additional aspects of performance to consider in order to produce more detailed and realistic estimates of predictive power. Finally, we describe the validation strategies that were employed by some of the most thoroughly validated and accessible target prediction methods.
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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.