ArticleJournal of cheminformatics2019
Dataset's chemical diversity limits the generalizability of machine learning predictions.
Article in Journal of cheminformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 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
39 citing papers in PubMed.
- Machine Learning-Guided Discovery of Natural BACE1 Inhibitors with Enzymatic and Cellular Validation.ACS omega · 2026Article
- Active Learning for Generalizable Detonation Performance Prediction of Energetic Materials.Chemistry of materials : a publication of the American Chemical Society · 2026Article
- Transfer learning of GW Bethe-Salpeter equation excitation energies.Chemical science · 2026Article
- Uncertainty Quantification forChemical reviews · 2026Review
- Recent Advances in Decoupling Strategies for Soft Sensors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Predicting SJournal of chemical theory and computation · 2026Article
- Discovery of Tetrahydroisoquinoline-Based SARS-CoV-2 Helicase Inhibitors with Iterative, Deep Learning-Enhanced Virtual Screening.Journal of chemical information and modeling · 2026Article
- 3d electron cloud descriptors for enhanced QSAR modeling of anti-colorectal cancer compounds.Journal of computer-aided molecular design · 2025Article
- Machine learning interatomic potentials in biomolecular modeling: principles, architectures, and applications.Biophysical reviews · 2025Review
- Quantum mechanical dataset of 836k neutral closed-shell molecules with up to 5 heavy atoms from C, N, O, F, Si, P, S, Cl, Br.Scientific data · 2025Article
- Active Learning Improves Ionization Efficiency Predictions and Quantification in Nontargeted LC/HRMS.Analytical chemistry · 2025Article
- RGBChem: Image-Like Representation of Chemical Compounds for Property Prediction.Journal of chemical theory and computation · 2025Article
- Pretraining graph transformers with atom-in-a-molecule quantum properties for improved ADMET modeling.Journal of cheminformatics · 2025Article
- QM9star, two Million DFT-computed Equilibrium Structures for Ions and Radicals with Atomic Information.Scientific data · 2024Article
- Quantum Chemistry Dataset with Ground- and Excited-state Properties of 450 Kilo Molecules.Scientific data · 2024Article
- Machine Learning Approach to Vertical Energy Gap in Redox Processes.Journal of chemical theory and computation · 2024Article
- Streamlining pipeline efficiency: a novel model-agnostic technique for accelerating conditional generative and virtual screening pipelines.Scientific reports · 2023Article
- Integrated Molecular Modeling and Machine Learning for Drug Design.Journal of chemical theory and computation · 2023Review
- Generative organic electronic molecular design informed by quantum chemistry.Chemical science · 2023Article
- Comprehensive exploration of graphically defined reaction spaces.Scientific data · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The QM9 dataset has become the golden standard for Machine Learning (ML) predictions of various chemical properties. QM9 is based on the GDB, which is a combinatorial exploration of the chemical space. ML molecular predictions have been recently published with an accuracy on par with Density Functional Theory calculations. Such ML models need to be tested and generalized on real data. PC9, a new QM9 equivalent dataset (only H, C, N, O and F and up to 9 "heavy" atoms) of the PubChemQC project is presented in this article. A statistical study of bonding distances and chemical functions shows that this new dataset encompasses more chemical diversity. Kernel Ridge Regression, Elastic Net and the Neural Network model provided by SchNet have been used on both datasets. The overall accuracy in energy prediction is higher for the QM9 subset. However, a model trained on PC9 shows a stronger ability to predict energies of the other dataset.
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.