ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2022
Pre-Training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- An image-based protein-ligand binding representation learning framework via multi-level flexible dynamics trajectory pre-training.Bioinformatics (Oxford, England) · 2025Article
- Artificial intelligence in drug development for delirium and Alzheimer's disease.Acta pharmaceutica Sinica. B · 2025Review
- Spatio-temporal learning from molecular dynamics simulations for protein-ligand binding affinity prediction.Bioinformatics (Oxford, England) · 2025Article
- Beyond static structures: protein dynamic conformations modeling in the post-AlphaFold era.Briefings in bioinformatics · 2025Review
- Bridging chemical space and biological efficacy: advances and challenges in applying generative models in structural modification of natural products.Natural products and bioprospecting · 2025Review
- Harnessing pre-trained models for accurate prediction of protein-ligand binding affinity.BMC bioinformatics · 2025Article
- Enhanced information cross-attention fusion for drug-target binding affinity prediction.PeerJ. Computer science · 2025Article
- Exploring Protein Conformational Changes Using a Large-Scale Biophysical Sampling Augmented Deep Learning Strategy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Prediction of protein-ligand binding affinity via deep learning models.Briefings in bioinformatics · 2024Review
- MpbPPI: a multi-task pre-training-based equivariant approach for the prediction of the effect of amino acid mutations on protein-protein interactions.Briefings in bioinformatics · 2023Article
- Improving molecular representation learning with metric learning-enhanced optimal transport.Patterns (New York, N.Y.) · 2023Article
- Pre-Training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- Holographic-(V)AE: An end-to-end SO(3)-equivariant (variational) autoencoder in Fourier space.Physical review researchArticle
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10 authors.
Funding
Abstract
The latest biological findings observe that the motionless "lock-and-key" theory is not generally applicable and that changes in atomic sites and binding pose can provide important information for understanding drug binding. However, the computational expenditure limits the growth of protein trajectory-related studies, thus hindering the possibility of supervised learning. A spatial-temporal pre-training method based on the modified equivariant graph matching networks, dubbed ProtMD which has two specially designed self-supervised learning tasks: atom-level prompt-based denoising generative task and conformation-level snapshot ordering task to seize the flexibility information inside molecular dynamics (MD) trajectories with very fine temporal resolutions is presented. The ProtMD can grant the encoder network the capacity to capture the time-dependent geometric mobility of conformations along MD trajectories. Two downstream tasks are chosen to verify the effectiveness of ProtMD through linear detection and task-specific fine-tuning. A huge improvement from current state-of-the-art methods, with a decrease of 4.3% in root mean square error for the binding affinity problem and an average increase of 13.8% in the area under receiver operating characteristic curve and the area under the precision-recall curve for the ligand efficacy problem is observed. The results demonstrate a strong correlation between the magnitude of conformation's motion in the 3D space and the strength with which the ligand binds with its receptor.
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