ArticleMolecules (Basel, Switzerland)2016
Bioactive Molecule Prediction Using Extreme Gradient Boosting.
Article in Molecules (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 1 of them a synthesis that pooled it.
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
70 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Classification of different light colors applied during the incubation period based on small intestine morphology with XGBoost algorithm.BMC veterinary research · 2026Article
- Pharmacokinetic Prediction of Repurposed Drugs for PDAC Using Artificial Intelligence.ACS omega · 2026Article
- AI accelerate the identification of druggable targets by 3D structures of proteins and compounds.NPJ precision oncology · 2026Review
- Neutrophil Percentage-to-Albumin Ratio as a Novel Prognostic Biomarker in Adult Diffuse Gliomas: Retrospective Study Integrating 3 Machine Learning Models and Cox Regression.JMIR medical informatics · 2026Article
- Physics informed machine learning for predictive toxicology and optimization of curcumin nanocarriers.Scientific reports · 2026Article
- MoleculeFormer is a GCN-transformer architecture for molecular property prediction.Communications biology · 2025Article
- Predicting the Optical Properties of Gold Nanoclusters Using Machine Learning Approach.ACS omega · 2025Article
- Article
- Automated drug design for druggable target identification using integrated stacked autoencoder and hierarchically self-adaptive optimization.Scientific reports · 2025Article
- Article
- MHNfs: Prompting In-Context Bioactivity Predictions for Low-Data Drug Discovery.Journal of chemical information and modeling · 2025Article
- hERG toxicity prediction in early drug discovery using extreme gradient boosting and isometric stratified ensemble mapping.Scientific reports · 2025Article
- Quantum-Mechanically Refined, Dynamics-Coupled, and AI-Augmented Elucidation of Epigenetic Inhibition: An In Silico Paradigm Targeting HDAC8 ofJournal of tropical medicine · 2025Article
- Explainable machine learning for predicting distant metastases in renal cell carcinoma patients: a population-based retrospective study.Frontiers in medicine · 2025Article
- Plant photosynthesis in basil (C3) and maize (C4) under different light conditions as basis of an AI-based model for PAM fluorescence/gas-exchange correlation.Frontiers in plant science · 2025Article
- Article
- Navigating tenses in Bengali sentences: A stacked ensemble model for enhanced prediction.PloS one · 2025Article
- Development of a machine learning model in prediction of the rapid progression of interstitial lung disease in patients with idiopathic inflammatory myopathy.Quantitative imaging in medicine and surgery · 2024Article
- Enhanced labor pain monitoring using machine learning and ECG waveform analysis for uterine contraction-induced pain.BioData mining · 2024Article
10 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Following the explosive growth in chemical and biological data, the shift from traditional methods of drug discovery to computer-aided means has made data mining and machine learning methods integral parts of today's drug discovery process. In this paper, extreme gradient boosting (Xgboost), which is an ensemble of Classification and Regression Tree (CART) and a variant of the Gradient Boosting Machine, was investigated for the prediction of biological activity based on quantitative description of the compound's molecular structure. Seven datasets, well known in the literature were used in this paper and experimental results show that Xgboost can outperform machine learning algorithms like Random Forest (RF), Support Vector Machines (LSVM), Radial Basis Function Neural Network (RBFN) and Naïve Bayes (NB) for the prediction of biological activities. In addition to its ability to detect minority activity classes in highly imbalanced datasets, it showed remarkable performance on both high and low diversity datasets.
Indexed as
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.