ArticleJournal of chemical information and modeling2019
Analyzing Learned Molecular Representations for Property Prediction.
Article in Journal of chemical information and modeling, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 587 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
587 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of molecular representation learning foundation models.Briefings in bioinformatics · 2026Pooled it
- Bridging Chemical Literature and Molecular Graphs: LLM-Augmented Enantioselectivity Prediction for Iridium-Catalyzed Asymmetric C-H Borylation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Binary interaction parameters of a million mixtures for PCP-SAFT using deep learning.Digital discovery · 2026Article
- Hierarchical generative modeling for the design of multi-component systems.Journal of cheminformatics · 2026Article
- Global descriptors informed learning for organic melting-point prediction.RSC advances · 2026Article
- A critical appraisal of structure-based virtual screening, binding free energy methods, and AI-driven molecular design across the evolving computational landscape of drug discovery.Journal of computer-aided molecular design · 2026Review
- COSMO-NET: fast and accurate machine learning surrogates for COSMO-based molecular descriptors.Molecular systems design & engineering · 2026Article
- Conformer-covariance diagnostics for aqueous solubility.Journal of computer-aided molecular design · 2026Article
- Cross Mol: cross-modal mask-predict pre-training for 3D molecular data.Bioinformatics (Oxford, England) · 2026Article
- Identification of YES1 Inhibitors From Approved Drugs Using a Multimodal Deep Learning Framework.Molecular informatics · 2026Article
- Target discovery and drug design in the era of artificial intelligence.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- Times are changing but order matters: transferable prediction of small-molecule liquid chromatography retention times.Nature methods · 2026Article
- Benchmarking machine learning and molecular dynamics methods for the prediction of the thermophysical properties of organic liquids.Digital discovery · 2026Article
- MEGPNM as a multiscale edge-aware GAT network with hybrid pooling predicting permeability of non-peptidic macrocycles.Communications chemistry · 2026Article
- Bridging BET bromodomain and immune checkpoint inhibitors through generative bioorganic frameworks for next-generation cancer immunotherapy.RSC medicinal chemistry · 2026Review
- Deep learning of tautomer stability from crystallographic proton positions.Chemical science · 2026Article
- A Machine Learning Approach for Predicting Synergism of Mixtures of Compounds in Treating Cancers.International journal of molecular sciences · 2026Article
- Physical priors improve performance of structure-based binding affinity models.bioRxiv : the preprint server for biology · 2026Article
- Artificial Intelligence-Guided Phenotypic Drug Repurposing Against Streptococcus pneumoniae.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Machine Learning-Guided Discovery of Bacterial-Selective Membrane-Active Compounds Reveals Mechanistic Bias in Antibiotic Training Datasets.bioRxiv : the preprint server for biology · 2026Article
527 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerprints or expert-crafted descriptors and graph convolutional neural networks that construct a learned molecular representation by operating on the graph structure of the molecule. However, recent literature has yet to clearly determine which of these two methods is superior when generalizing to new chemical space. Furthermore, prior research has rarely examined these new models in industry research settings in comparison to existing employed models. In this paper, we benchmark models extensively on 19 public and 16 proprietary industrial data sets spanning a wide variety of chemical end points. In addition, we introduce a graph convolutional model that consistently matches or outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary data sets. Our empirical findings indicate that while approaches based on these representations have yet to reach the level of experimental reproducibility, our proposed model nevertheless offers significant improvements over models currently used in industrial workflows.
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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.