ArticleJournal of cheminformatics2020
Building attention and edge message passing neural networks for bioactivity and physical-chemical property prediction.
Article in Journal of cheminformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 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.
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Who cites it
43 citing papers in PubMed.
- A Comparative Evaluation Framework Integrating Machine Learning and Deep Learning Models with ADME-Based Pharmacokinetic Assessment for HIV-Related Compounds.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Blood‒brain barrier permeability assessment: from biomimetic models to multimodal imaging.Journal of Zhejiang University. Science. B · 2026Review
- A unified multi-scale deep learning framework for molecular property prediction that bridges molecular structures and fingerprinting.Communications chemistry · 2026Article
- Sensing Compound Substructures Combined with Molecular Fingerprinting to Predict Drug-Target Interactions.Interdisciplinary sciences, computational life sciences · 2026Article
- Fingerprint-enhanced hierarchical molecular graph neural networks for property prediction.Journal of pharmaceutical analysis · 2025Article
- Machine learning applications for thermochemical and kinetic property prediction.Reviews in chemical engineering · 2025Review
- Predicting Toxicity toward Nitrifiers by Attention-Enhanced Graph Neural Networks and Transfer Learning from Baseline Toxicity.Environmental science & technology · 2025Article
- MultiChem: predicting chemical properties using multi-view graph attention network.BioData mining · 2025Article
- A systematic review of data and models for predicting food flavor and texture.Current research in food science · 2025Review
- MFE-DDI: A multi-view feature encoding framework for drug-drug interaction prediction.Computational and structural biotechnology journal · 2025Article
- G-Computational and structural biotechnology journal · 2024Article
- Multimodal fused deep learning for drug property prediction: Integrating chemical language and molecular graph.Computational and structural biotechnology journal · 2024Article
- Article
- Using DeepSignalingFlow to mine signaling flows interpreting mechanism of synergy of cocktails.NPJ systems biology and applications · 2024Article
- CycPeptMP: enhancing membrane permeability prediction of cyclic peptides with multi-level molecular features and data augmentation.Briefings in bioinformatics · 2024Article
- A deep position-encoding model for predicting olfactory perception from molecular structures and electrostatics.NPJ systems biology and applications · 2024Article
- Gram matrix: an efficient representation of molecular conformation and learning objective for molecular pretraining.Briefings in bioinformatics · 2024Article
- Transfer learning for a foundational chemistry model.Chemical science · 2024Article
- Large-scale comparison of machine learning methods for profiling prediction of kinase inhibitors.Journal of cheminformatics · 2024Article
- Predictive Minisci late stage functionalization with transfer learning.Nature communications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Neural Message Passing for graphs is a promising and relatively recent approach for applying Machine Learning to networked data. As molecules can be described intrinsically as a molecular graph, it makes sense to apply these techniques to improve molecular property prediction in the field of cheminformatics. We introduce Attention and Edge Memory schemes to the existing message passing neural network framework, and benchmark our approaches against eight different physical-chemical and bioactivity datasets from the literature. We remove the need to introduce a priori knowledge of the task and chemical descriptor calculation by using only fundamental graph-derived properties. Our results consistently perform on-par with other state-of-the-art machine learning approaches, and set a new standard on sparse multi-task virtual screening targets. We also investigate model performance as a function of dataset preprocessing, and make some suggestions regarding hyperparameter selection.
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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.