ArticleChem2020
Learning to Make Chemical Predictions: the Interplay of Feature Representation, Data, and Machine Learning Methods.
Article in Chem, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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.
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
27 citing papers in PubMed.
- Unlocking the door for synergizing expansion-capacity in Si anodes: breathable flexible MOFs.Chemical science · 2026Review
- MSGRL: A Motif-Driven Self-Supervised Graph Representation Learning Framework for Interpretable Molecular Property Prediction.Molecules (Basel, Switzerland) · 2026Article
- TMolNet: a task-aware multimodal neural network for molecular property prediction.Molecular diversity · 2026Article
- Article
- A conformational benchmark for optical property prediction with solvent-aware graph neural networks.Communications chemistry · 2026Article
- Selector: A General Python Library for Diverse Subset Selection.Journal of chemical information and modeling · 2026Article
- Leak Proof PDBBind: A Reorganized Data Set of Protein-Ligand Complexes for More Generalizable Binding Affinity Prediction.The journal of physical chemistry. B · 2026Article
- Selector: A General Python Library for Diverse Subset Selection.bioRxiv : the preprint server for biology · 2025Article
- Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.Chemical research in toxicology · 2025Review
- UCBShift 2.0: Bridging the Gap from Backbone to Side Chain Protein Chemical Shift Prediction for Protein Structures.Journal of the American Chemical Society · 2024Article
- Exploring an accurate machine learning model to quickly estimate stability of diverse energetic materials.iScience · 2024Article
- Highly Accurate Prediction of NMR Chemical Shifts from Low-Level Quantum Mechanics Calculations Using Machine Learning.Journal of chemical theory and computation · 2024Article
- Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis.Beilstein journal of organic chemistry · 2024Review
- Integrated Molecular Modeling and Machine Learning for Drug Design.Journal of chemical theory and computation · 2023Review
- Accurate, interpretable predictions of materials properties within transformer language models.Patterns (New York, N.Y.) · 2023Article
- Force field-inspired transformer network assisted crystal density prediction for energetic materials.Journal of cheminformatics · 2023Article
- Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review.International journal of molecular sciences · 2023Review
- Retention time prediction for chromatographic enantioseparation by quantile geometry-enhanced graph neural network.Nature communications · 2023Article
- Multitask Deep Ensemble Prediction of Molecular Energetics in Solution: From Quantum Mechanics to Experimental Properties.Journal of chemical theory and computation · 2023Article
- Application of Machine Learning in a Mineral Leaching Process-Taking Pyrolusite Leaching as an Example.ACS omega · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Recently supervised machine learning has been ascending in providing new predictive approaches for chemical, biological and materials sciences applications. In this Perspective we focus on the interplay of machine learning method with the chemically motivated descriptors and the size and type of data sets needed for molecular property prediction. Using Nuclear Magnetic Resonance chemical shift prediction as an example, we demonstrate that success is predicated on the choice of feature extracted or real-space representations of chemical structures, whether the molecular property data is abundant and/or experimentally or computationally derived, and how these together will influence the correct choice of popular machine learning methods drawn from deep learning, random forests, or kernel methods.
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.