ArticleNature communications2024
Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Hierarchical geometric deep learning enables scalable analysis of molecular dynamics.The Journal of chemical physics · 2026Article
- Modeling dual-range atomic interactions with physicochemical principles for molecular force fields.Bioinformatics (Oxford, England) · 2026Article
- PETIMOT: a novel framework for inferring protein motions from sparse data using SE(3)-equivariant graph neural networks.Acta crystallographica. Section D, Structural biology · 2026Article
- How Advanced Artificial Intelligence Technologies Shape Drug-Drug and Drug-Target Interaction Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Knowledge Distillation of a Protein Language Model Yields a Foundational Implicit Solvent Model.Journal of chemical theory and computation · 2026Article
- Deep residual learning for molecular force fields.Nature communications · 2026Article
- Efficient Parallelization of Message Passing Neural Network Potentials for Large-Scale Molecular Dynamics.JACS Au · 2026Article
- A Controlled Study of Physics-Informed Auxiliary Supervision and Scalar Triplet Attention in Equivariant Molecular Force Fields.Molecules (Basel, Switzerland) · 2026Article
- ITMol: a molecular image-text foundation model bridging the semantic gap for property prediction and retrieval.BMC biology · 2026Article
- DPLM: Dynamics-aware Protein Language Model via contrastive learning between sequence and molecular dynamics simulation trajectory.bioRxiv : the preprint server for biology · 2026Article
- Transfer learning of GW Bethe-Salpeter equation excitation energies.Chemical science · 2026Article
- DeepMCL-DTI: predicting drug-target interactions using multi-channel deep learning with attention mechanism.Molecular diversity · 2026Article
- Knowledge Distillation of a Protein Language Model Yields a Foundational Implicit Solvent Model.ArXiv · 2026Article
- A scalable and quantum-accurate foundation model for biomolecular force fields via linearly tensorized quadrangle attention.Nature communications · 2026Article
- A conformational benchmark for optical property prediction with solvent-aware graph neural networks.Communications chemistry · 2026Article
- Efficient Sampling of Short Protein Trajectories with Conditional Diffusion Models.Journal of chemical theory and computation · 2026Article
- AI-Resolved Protein Energy Landscapes, Electrodynamics, and Fluidic Microcircuits as a Unified Framework for Predicting Neurodegeneration.International journal of molecular sciences · 2026Review
- Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules Versus Therapeutic Peptides.Biology · 2025Review
- Enhanced Prediction of Absorption and Emission Wavelengths of Organic Compounds through Hybrid Graph Neural Network Architectures.ACS omega · 2025Article
- Machine learning interatomic potentials in biomolecular modeling: principles, architectures, and applications.Biophysical reviews · 2025Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Geometric deep learning has been revolutionizing the molecular modeling field. Despite the state-of-the-art neural network models are approaching ab initio accuracy for molecular property prediction, their applications, such as drug discovery and molecular dynamics (MD) simulation, have been hindered by insufficient utilization of geometric information and high computational costs. Here we propose an equivariant geometry-enhanced graph neural network called ViSNet, which elegantly extracts geometric features and efficiently models molecular structures with low computational costs. Our proposed ViSNet outperforms state-of-the-art approaches on multiple MD benchmarks, including MD17, revised MD17 and MD22, and achieves excellent chemical property prediction on QM9 and Molecule3D datasets. Furthermore, through a series of simulations and case studies, ViSNet can efficiently explore the conformational space and provide reasonable interpretability to map geometric representations to molecular structures.
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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.