ArticleNature communications2025
A self-conformation-aware pre-training framework for molecular property prediction with substructure interpretability.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Genome-wide-scale prediction of compound-protein interactions using foundation and language models based on three-dimensional structures of compounds and proteins.Bioinformatics (Oxford, England) · 2026Article
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- MEGPNM as a multiscale edge-aware GAT network with hybrid pooling predicting permeability of non-peptidic macrocycles.Communications chemistry · 2026Article
- Task-adaptive multimodal molecular representations for structure-sensitive property prediction.Chemical science · 2026Article
- MSF-HierGNN: a multi-source substructure-fusion hierarchical GNN method and web server to predict molecular property for drug design.Briefings in bioinformatics · 2026Article
- DECANT: decoupling mechanism from context in single-cell drug perturbation representation.Bioinformatics (Oxford, England) · 2026Article
- iBitter-HF: A Method for Bitter Peptide Sequence Identification Based on Hybrid Feature Embedding.Foods (Basel, Switzerland) · 2026Article
- Integrated Transcriptomic Analyses Identify Four Prognosis-Associated Genes in Hepatocellular Carcinoma.International journal of molecular sciences · 2026Article
- Hyp-retro: hypernetwork-guided preference-conditioned retrosynthetic planner.Molecular diversity · 2026Article
- Drug target prediction from perturbation transcriptomics via a biological function-guided hypergraph siamese network.Bioinformatics (Oxford, England) · 2026Article
- Enhancing cross-context generalization in drug perturbation prediction with a multimodal conditional diffusion framework.Bioinformatics (Oxford, England) · 2026Article
- CataCon: a contrastive graph representation learning framework for catalyst prediction.Journal of cheminformatics · 2026Article
- MsgaBpred: A B-cell epitope predictor integrating AlphaFold3-predicted structures with multi-scale GCNs and pre-trained language model ESM-C.PLoS computational biology · 2026Article
- Uncertainty-aware hybrid deep generative framework for robust and explainable drug discovery.BMC bioinformatics · 2026Article
- PepLM-GNN: A graph neural network framework leveraging pre-trained language models for peptide-protein binding prediction.PLoS computational biology · 2026Article
- CFGSCDSA: Predicting circRNA-drug sensitivity associations based on collaborative feature learning and graph structure learning.PLoS computational biology · 2026Article
- Spectral Decomposition of Chemical Semantics for Activity Cliffs-Aware Molecular Property Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- DeepSGE: predicting spatial gene expression using residual network with efficient channel attention and dynamic graph attention network.BMC genomics · 2026Article
- UniSyn: a multi-modal framework with knowledge transfer for anti-cancer drug synergy prediction.Genome biology · 2026Article
- FKSUDDAPre: A drug-disease association prediction framework based on F-TEST feature selection and AMDKSU resampling with interpretability analysis.PLoS computational biology · 2026Article
Corrections and comments
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Authors and funding
12 authors.
Funding
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Abstract
The major challenges in drug development stem from frequent structure-activity cliffs and unknown drug properties, which are expensive and time-consuming to estimate, contributing to a high rate of failures and substantial unavoidable costs in the clinical phases. Herein, we propose the self-conformation-aware graph transformer (SCAGE), an innovative deep learning architecture pretrained with approximately 5 million drug-like compounds for molecular property prediction. Notably, we develop a multitask pretraining framework, which incorporates four supervised and unsupervised tasks: molecular fingerprint prediction, functional group prediction using chemical prior information, 2D atomic distance prediction, and 3D bond angle prediction, covering aspects from molecular structures to functions. It enables learning comprehensive conformation-aware prior knowledge, thereby enhancing its generalization across various molecular property tasks. Moreover, we design a data-driven multiscale conformational learning strategy that effectively guides the model in understanding and representing atomic relationships at the molecular conformational scale. SCAGE achieves significant performance improvements across 9 molecular properties and 30 structure-activity cliff benchmarks. Case studies demonstrate that SCAGE accurately captures crucial functional groups at the atomic level, which are closely associated with molecular activity, providing valuable insights into quantitative structure-activity relationships.
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