ArticleJournal of computer-aided molecular design2021
Comparing predictive ability of QSAR/QSPR models using 2D and 3D molecular representations.
Article in Journal of computer-aided molecular design, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Conformer-covariance diagnostics for aqueous solubility.Journal of computer-aided molecular design · 2026Article
- Cardiosim-Tox: an interpretable multitask deep learning QSAR platform with multimodal feature fusion for predicting hERG, Cav1.2, and Nav1.5 blockade risk and potency.Archives of toxicology · 2026Article
- SynergisticiScience · 2026Article
- Article
- ML-Driven Pharmaceutical Cocrystal Technology: Advances in Screening, Property Prediction and Applications.AAPS PharmSciTech · 2026Review
- Enhancing molecular property prediction through data integration and consistency assessment.Journal of cheminformatics · 2025Article
- Prediction of blood-brain barrier and Caco-2 permeability through the Enalos Cloud Platform: combining contrastive learning and atom-attention message passing neural networks.Journal of cheminformatics · 2025Article
- Understanding Conformation Importance in Data-Driven Property Prediction Models.Journal of chemical information and modeling · 2025Article
- Mayer-Homology Learning Prediction of Protein-Ligand Binding Affinities.Journal of computational biophysics and chemistry · 2025Article
- Prediction of the Extent of Blood-Brain Barrier Transport Using Machine Learning and Integration into the LeiCNS-PK3.0 Model.Pharmaceutical research · 2025Article
- An overview on olfaction in the biological, analytical, computational, and machine learning fields.Archiv der Pharmazie · 2025Review
- Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors.Computational and structural biotechnology journal · 2024Review
- QSAR Regression Models for Predicting HMG-CoA Reductase Inhibition.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning.Journal of cheminformatics · 2024Article
- Ligand-based approaches to activity prediction for the early stage of structure-activity-relationship progression.Journal of computer-aided molecular design · 2022Article
- Natural product drug discovery in the artificial intelligence era.Chemical science · 2022Review
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) models predict biological activity and molecular property based on the numerical relationship between chemical structures and activity (property) values. Molecular representations are of importance in QSAR/QSPR analysis. Topological information of molecular structures is usually utilized (2D representations) for this purpose. However, conformational information seems important because molecules are in the three-dimensional space. As a three-dimensional molecular representation applicable to diverse compounds, similarity between a test molecule and a set of reference molecules has been previously proposed. This 3D representation was found to be effective on virtual screening for early enrichment of active compounds. In this study, we introduced the 3D representation into QSAR/QSPR modeling (regression tasks). Furthermore, we investigated relative merits of 3D representations over 2D in terms of the diversity of training data sets. For the prediction task of quantum mechanics-based properties, the 3D representations were superior to 2D. For predicting activity of small molecules against specific biological targets, no consistent trend was observed in the difference of performance using the two types of representations, irrespective of the diversity of training data sets.
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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.