ArticleWiley interdisciplinary reviews. Computational molecular science2014
Machine learning methods in chemoinformatics.
Article in Wiley interdisciplinary reviews. Computational molecular science, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 119 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
119 citing papers in PubMed.
- Active Learning for Generalizable Detonation Performance Prediction of Energetic Materials.Chemistry of materials : a publication of the American Chemical Society · 2026Article
- Deciphering Molecular and Solvent Effects on Aqueous and Organic Solubility through Interpretable Machine Learning Approaches.ACS omega · 2026Article
- Uncertainty Quantification forChemical reviews · 2026Review
- Predicting peroxisome proliferator-activated receptor gamma potency of small molecules: a synergistic consensus model and deep learning binding affinity approach powered by Enalos Cloud Platform.Molecular diversity · 2026Article
- Prediction of graft outcomes after kidney transplantation: When standard statistics compare to machine learning techniques.World journal of nephrology · 2026Article
- Antibacterial Drug Discovery: Deep Learning Successes and Challenges through the Structural Biology Lens.Computational and structural biotechnology journal · 2026Review
- Designing Potent HIV‑1 Protease Inhibitors Using Machine Learning and QSAR Approaches.ACS omega · 2025Article
- Uncovering molecular determinants of potency and binding affinity in hit compounds targeting FGF14/Nav1.6 complex.Journal of cheminformatics · 2025Article
- MolAI: A Deep Learning Framework for Data-Driven Molecular Descriptor Generation and Advanced Drug Discovery Applications.Journal of chemical information and modeling · 2025Article
- Prediction of Standard Combustion Enthalpy of Organic Compounds Combining Machine Learning and Chemical Graph Theory: A Strategy.ACS omega · 2025Article
- TRPM8 Protein Dynamics Correlates with Ligand Structure and Cellular Function.Journal of the American Chemical Society · 2025Article
- Enhancing molecular property prediction with quantized GNN models.Journal of cheminformatics · 2025Article
- Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.Chemical research in toxicology · 2025Review
- TRPM8 protein dynamics correlates with ligand structure and cellular function.bioRxiv : the preprint server for biology · 2025Article
- A Perspective on Foundation Models in Chemistry.JACS Au · 2025Review
- Unraveling the Mysteries of Alzheimer's Disease Using Artificial Intelligence.Reviews on recent clinical trials · 2025Review
- ML-based Models as a Strategy to Discover Novel Antiepileptic Drugs Targeting Sodium Receptor Channel.Current topics in medicinal chemistry · 2025Article
- Exploring Potential Bifunctional Peptides with Anti-tyrosinase and Antioxidant Activities fromCombinatorial chemistry & high throughput screening · 2025Article
- Three Dihydroquinolin-4-one Derivatives as Potential Biodiesel Additives: From the Molecular Structure to Machine Learning Approach.ACS omega · 2024Article
- Design and predict the potential of imidazole-based organic dyes in dye-sensitized solar cells using fingerprint machine learning and supported by a web application.Scientific reports · 2024Article
59 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion. Though particular machine learning methods are popular in chemoinformatics and quantitative structure-activity relationships (QSAR), many others exist in the technical literature. This discussion is methods-based and focused on some algorithms that chemoinformatics researchers frequently use. It makes no claim to be exhaustive. We concentrate on methods for supervised learning, predicting the unknown property values of a test set of instances, usually molecules, based on the known values for a training set. Particularly relevant approaches include Artificial Neural Networks, Random Forest, Support Vector Machine, k-Nearest Neighbors and naïve Bayes classifiers.
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.