ArticleJournal of cheminformatics2023
Double-head transformer 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 11 papers.
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Who cites it
11 citing papers in PubMed, 17 citations in OpenAlex.
- Unifying pJournal of chemical information and modeling · 2026Article
- Multimodal molecular representation and property prediction based on structured complementary feature fusion.Frontiers in pharmacology · 2026Article
- Artificial intelligence in drug development for delirium and Alzheimer's disease.Acta pharmaceutica Sinica. B · 2025Review
- Advancing ADMET prediction through multiscale fragment-aware pretraining with MSformer-ADMET.Briefings in bioinformatics · 2025Article
- A review of transformer models in drug discovery and beyond.Journal of pharmaceutical analysis · 2025Review
- 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
- Chemical Graph-Based Transformer Models for Yield Prediction of High-Throughput Cross-Coupling Reaction Datasets.ACS omega · 2024Article
- PharmaBench: Enhancing ADMET benchmarks with large language models.Scientific data · 2024Article
- Hybrid fragment-SMILES tokenization for ADMET prediction in drug discovery.BMC bioinformatics · 2024Article
- Attention is all you need: utilizing attention in AI-enabled drug discovery.Briefings in bioinformatics · 2023Review
- Meta-learning for transformer-based prediction of potent compounds.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Existing molecular property prediction methods based on deep learning ignore the generalization ability of the nonlinear representation of molecular features and the reasonable assignment of weights of molecular features, making it difficult to further improve the accuracy of molecular property prediction. To solve the above problems, an end-to-end double-head transformer neural network (DHTNN) is proposed in this paper for high-precision molecular property prediction. For the data distribution characteristics of the molecular dataset, DHTNN specially designs a new activation function, beaf, which can greatly improve the generalization ability of the nonlinear representation of molecular features. A residual network is introduced in the molecular encoding part to solve the gradient explosion problem and ensure that the model can converge quickly. The transformer based on double-head attention is used to extract molecular intrinsic detail features, and the weights are reasonably assigned for predicting molecular properties with high accuracy. Our model, which was tested on the MoleculeNet [1] benchmark dataset, showed significant performance improvements over other state-of-the-art methods.
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