ReviewChemical science2025
A review of machine learning methods for imbalanced data challenges in chemistry.
Review in Chemical science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed.
- Artificial Intelligence-Driven Inverse Design of Singlet Fission Candidates in the Acene family.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A study on eye movement trajectory classification for strabismus screening via integration of global dependencies and temporal dynamics.Medical & biological engineering & computing · 2026Article
- Transforming Molecular Synthesis With Large Language Models.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Review
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- Machine Learning Accelerated Computational Design of Bio-Inspired Catalysts in the Nitrogen Reduction Reaction.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Construction of a curated human pharmacokinetics database for molecular fragment analysis and machine learning applications.Drug metabolism and disposition: the biological fate of chemicals · 2026Article
- BRPtools: An AutoML-Powered web platform for multiclass disease prediction from bulk blood RNA-seq data.Molecular therapy. Nucleic acids · 2026Article
- AI-Driven Discovery of Prototype CLEC4M Inhibitors Targeting Marburg Virus Entry via Integrated Machine Learning and Molecular Modeling.International journal of molecular sciences · 2026Article
- Decoding Structure-Property Relationships in Anion Exchange Membranes via a Chemically Informed Dual-Channel Graph Attention Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Machine learning based approaches for structure activity relationship analysis of heparanase inhibitors.Scientific reports · 2026Article
- Reducing Video Verification Burden: Machine Learning Classification of Head Acceleration Events in Youth Football.Research square · 2026Article
- Artificial Intelligence Predictions in Huge Chemical Spaces: Chiroptical Properties of [6]-helicene Family.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Yield Prediction of Organic Reactions in Biased Data Sets via Positive-Unlabeled Learning.Journal of the American Chemical Society · 2026Article
- Predicting camouflage treatment outcomes in skeletal class III malocclusion using machine learning.Scientific reports · 2026Article
- Discovery of TDP-43 aggregation inhibitorsbioRxiv : the preprint server for biology · 2026Article
- Machine learning-driven exosome-mimetic lipid nanoparticles for tumor-specific targeting.Nano convergence · 2026Article
- Bayesian Youden index for algorithmic evaluation under class imbalance: mathematical foundations with applications to insulin resistance and diabetes progression.Frontiers in epidemiology · 2026Article
- Predicting blood-brain barrier permeability of chemicals by machine learning modeling.NAM journal · 2026Article
- Adaptive resampling for improved machine learning in imbalanced single-cell datasets.bioRxiv : the preprint server for biology · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Imbalanced data, where certain classes are significantly underrepresented in a dataset, is a widespread machine learning (ML) challenge across various fields of chemistry, yet it remains inadequately addressed. This data imbalance can lead to biased ML or deep learning (DL) models, which fail to accurately predict the underrepresented classes, thus limiting the robustness and applicability of these models. With the rapid advancement of ML and DL algorithms, several promising solutions to this issue have emerged, prompting the need for a comprehensive review of current methodologies. In this review, we examine the prominent ML approaches used to tackle the imbalanced data challenge in different areas of chemistry, including resampling techniques, data augmentation techniques, algorithmic approaches, and feature engineering strategies. Each of these methods is evaluated in the context of its application across various aspects of chemistry, such as drug discovery, materials science, cheminformatics, and catalysis. We also explore future directions for overcoming the imbalanced data challenge and emphasize data augmentation
Identifiers
What OpenQuestion holds
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.