ArticleJournal of cheminformatics2023
ABT-MPNN: an atom-bond transformer-based message-passing neural network for molecular property prediction.
Article in Journal of cheminformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.
- Knowledge mapping of graph neural networks for drug discovery: a bibliometric and visualized analysis.Frontiers in pharmacology · 2024Pooled it
- A survey of transformer and LLM-based architectures, workflows, and systems in drug discovery.Journal of computer-aided molecular design · 2026Review
- A unified framework for molecular property prediction based on hierarchical multi-granularity molecular representation learning.Bioinformatics (Oxford, England) · 2026Article
- Geometric Deep Learning-Based Drug Design Models for Small-Molecule Drug Discovery.Molecular informatics · 2026Review
- Multiscale-Aware Graph Embedding Approach Uncovers LC-61, a Potent Anti-Journal of chemical information and modeling · 2026Article
- GNN-based trust propagation and intelligent certificate revocation decision mechanism for large-scale IoT networks.Scientific reports · 2026Article
- AI-Driven Acceleration of Fluorescence Probe Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Artificial intelligence in drug discovery: from algorithmic foundations to clinical translation.Frontiers in pharmacology · 2026Review
- A message passing framework for precise cell state identification with scClassify2.Genome biology · 2025Article
- Heuristic optimization in classification atoms in molecules using GCN via uniform simulated annealing.Scientific reports · 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
- A new strategy for Cas protein recognition based on graph neural networks and SMILES encoding.Scientific reports · 2025Article
- A modular artificial intelligence framework to facilitate fluorophore design.Nature communications · 2025Article
- Predicting Toxicity toward Nitrifiers by Attention-Enhanced Graph Neural Networks and Transfer Learning from Baseline Toxicity.Environmental science & technology · 2025Article
- hERGAT: predicting hERG blockers using graph attention mechanism through atom- and molecule-level interaction analyses.Journal of cheminformatics · 2025Article
- STNGS: a deep scaffold learning-driven generation and screening framework for discovering potential novel psychoactive substances.Briefings in bioinformatics · 2024Article
- DrugDAGT: a dual-attention graph transformer with contrastive learning improves drug-drug interaction prediction.BMC biology · 2024Article
- Calculation of solvation force in molecular dynamics simulation by deep-learning method.Biophysical journal · 2024Article
- Revisiting the Application of Machine Learning Approaches in Predicting Aqueous Solubility.ACS omega · 2024Article
- GR-pKa: a message-passing neural network with retention mechanism for pKa prediction.Briefings in bioinformatics · 2024Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Graph convolutional neural networks (GCNs) have been repeatedly shown to have robust capacities for modeling graph data such as small molecules. Message-passing neural networks (MPNNs), a group of GCN variants that can learn and aggregate local information of molecules through iterative message-passing iterations, have exhibited advancements in molecular modeling and property prediction. Moreover, given the merits of Transformers in multiple artificial intelligence domains, it is desirable to combine the self-attention mechanism with MPNNs for better molecular representation. We propose an atom-bond transformer-based message-passing neural network (ABT-MPNN), to improve the molecular representation embedding process for molecular property predictions. By designing corresponding attention mechanisms in the message-passing and readout phases of the MPNN, our method provides a novel architecture that integrates molecular representations at the bond, atom and molecule levels in an end-to-end way. The experimental results across nine datasets show that the proposed ABT-MPNN outperforms or is comparable to the state-of-the-art baseline models in quantitative structure-property relationship tasks. We provide case examples of Mycobacterium tuberculosis growth inhibitors and demonstrate that our model's visualization modality of attention at the atomic level could be an insightful way to investigate molecular atoms or functional groups associated with desired biological properties. The new model provides an innovative way to investigate the effect of self-attention on chemical substructures and functional groups in molecular representation learning, which increases the interpretability of the traditional MPNN and can serve as a valuable way to investigate the mechanism of action of drugs.
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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.