ArticleJournal of chemical theory and computation2021
TorchMD: A Deep Learning Framework for Molecular Simulations.
Article in Journal of chemical theory and computation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
73 citing papers in PubMed.
- Artificial intelligence catalyzes antimicrobial peptide design.Synthetic and systems biotechnology · 2027Review
- Elucidating the Structure and Activation Mechanisms of GPCRs Using Modern Computational and AI Tools.Cell biochemistry and biophysics · 2026Review
- s_mmpbsa: A Lite and Cross-Platform MM-PBSA Program.Molecules (Basel, Switzerland) · 2026Article
- Active subspace learning for coarse-grained molecular dynamics.The Journal of chemical physics · 2026Article
- Iron-Catalyzed Chlorination of Titanium Oxides in Molten Salts: A Deep Neural Network-Based Mechanistic Study.Materials (Basel, Switzerland) · 2026Article
- Disentangled autoencoding equivariant diffusion model for controlled generation of 3D molecules.Nature communications · 2026Article
- The rise and future of peptide-based antimicrobials.Journal of microbiology (Seoul, Korea) · 2026Review
- Generating Dynamic Structures Through Physics-Based Sampling of Predicted Inter-Residue Geometries.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Data set Generation for Training Machine-Learned Interatomic Potentials.Journal of chemical theory and computation · 2026Article
- AI-Driven Protein Research: From Prediction to Design.Methods in molecular biology (Clifton, N.J.) · 2026Review
- Artificial intelligence in migraine: a narrative review on the diagnostic, prognostic, and therapeutic applications.Oxford open neuroscience · 2026Article
- A multi-grained symmetric differential equation model for learning protein-ligand binding dynamics.Nature communications · 2025Article
- Generative diffusion model surrogates for mechanistic agent-based biological models.Machine learning: science and technology · 2025Article
- Active subspace learning for coarse-grained molecular dynamics.bioRxiv : the preprint server for biology · 2025Article
- Condensation of Force Field Parameters from Machine Learning Predicted Distributions for High-Throughput Virtual Screening Applications.Journal of chemical information and modeling · 2025Article
- Artificial intelligence in protein-based detection and inhibition of AMR pathways.Journal of computer-aided molecular design · 2025Review
- Surrogate modeling of Cellular-Potts agent-based models as a segmentation task using the U-Net neural network architecture.PLoS computational biology · 2025Article
- Machine learning interatomic potentials in biomolecular modeling: principles, architectures, and applications.Biophysical reviews · 2025Review
- Development of Coarse-Grained Lipid Force Fields Based on a Graph Neural Network.Journal of chemical theory and computation · 2025Article
- The Enduring Relevance of Semiempirical Quantum Mechanics.The journal of physical chemistry. A · 2025Review
13 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability of such potentials can be improved leveraging data-driven models derived with machine learning approaches. Here, we present TorchMD, a framework for molecular simulations with mixed classical and machine learning potentials. All force computations including bond, angle, dihedral, Lennard-Jones, and Coulomb interactions are expressed as PyTorch arrays and operations. Moreover, TorchMD enables learning and simulating neural network potentials. We validate it using standard Amber all-atom simulations, learning an ab initio potential, performing an end-to-end training, and finally learning and simulating a coarse-grained model for protein folding. We believe that TorchMD provides a useful tool set to support molecular simulations of machine learning potentials. Code and data are freely available at github.com/torchmd.
Identifiers
What OpenQuestion holds
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.