ArticleNature communications2021
Algebraic graph-assisted bidirectional transformers for molecular property prediction.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 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.
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Who cites it
63 citing papers in PubMed.
- Activity-cliff awareness enables robust graph learning for molecular property prediction.Nature communications · 2026Article
- DeepSnap: From Three-Dimensional Molecular Images to Quantitative Structure-Activity Predictions.International journal of molecular sciences · 2026Review
- Toward an AI Era: Application of Artificial Intelligence in Inclusion Complex Screening.Pharmaceutics · 2026Review
- Martini Mapper: An Automated Fragment-Based Mapping Algorithm for Developing Coarse-Grained Models within the Martini 3 Framework.Journal of chemical information and modeling · 2026Article
- A unified multi-scale deep learning framework for molecular property prediction that bridges molecular structures and fingerprinting.Communications chemistry · 2026Article
- A geometric graph-based deep learning model for drug-target affinity prediction.BMC bioinformatics · 2025Article
- From Deep-Sea Natural Product to Optimized Therapeutics: Computational Design of Marizomib Analogs.International journal of molecular sciences · 2025Article
- Rescuing Verubecestat: An Integrative Molecular Modeling and Simulation Approach for Designing Next-Generation BACE1 Inhibitors.International journal of molecular sciences · 2025Article
- Machine Learning Approaches in Soft Matter Molecular Simulation and Materials Characterization: Challenges and Perspectives.ChemPlusChem · 2025Article
- A Review of Topological Data Analysis and Topological Deep Learning in Molecular Sciences.Journal of chemical information and modeling · 2025Review
- Article
- Q-GEM: Quantum Chemistry Knowledge Fusion Geometry-Enhanced Molecular Representation for Property Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Drug Resistance Predictions Based on a Directed Flag Transformer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- A multiscale molecular structural neural network for molecular property prediction.Molecular diversity · 2025Article
- Digital Alchemy: The Rise of Machine and Deep Learning in Small-Molecule Drug Discovery.International journal of molecular sciences · 2025Review
- A review of transformer models in drug discovery and beyond.Journal of pharmaceutical analysis · 2025Review
- Fingerprint-enhanced hierarchical molecular graph neural networks for property prediction.Journal of pharmaceutical analysis · 2025Article
- A review of machine learning methods for imbalanced data challenges in chemistry.Chemical science · 2025Review
- Proteomic Learning of Gamma-Aminobutyric Acid (GABA) Receptor-Mediated Anesthesia.Journal of chemical information and modeling · 2025Article
- Category-specific topological learning of metal-organic frameworks.Journal of materials chemistry. A · 2025Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The ability of molecular property prediction is of great significance to drug discovery, human health, and environmental protection. Despite considerable efforts, quantitative prediction of various molecular properties remains a challenge. Although some machine learning models, such as bidirectional encoder from transformer, can incorporate massive unlabeled molecular data into molecular representations via a self-supervised learning strategy, it neglects three-dimensional (3D) stereochemical information. Algebraic graph, specifically, element-specific multiscale weighted colored algebraic graph, embeds complementary 3D molecular information into graph invariants. We propose an algebraic graph-assisted bidirectional transformer (AGBT) framework by fusing representations generated by algebraic graph and bidirectional transformer, as well as a variety of machine learning algorithms, including decision trees, multitask learning, and deep neural networks. We validate the proposed AGBT framework on eight molecular datasets, involving quantitative toxicity, physical chemistry, and physiology datasets. Extensive numerical experiments have shown that AGBT is a state-of-the-art framework for molecular property prediction.
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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.