TorchMD: A Deep Learning Framework for Molecular Simulations

Doerr, Stefan, Majewski, Maciej, Pérez, Adrià, Krämer, Andreas, Clementi, Cecilia, Noe, Frank, Giorgino, Toni, De Fabritiis, Gianni·2021·Journal of Chemical Theory and Computation

Cited by (230)

Items 201–230 of 230

  1. PREFMoDeL: A Systematic Review and Proposed Taxonomy of Biomolecular Features for Deep Learning
    Jacob L. North, Victor L. Hsu · 2023 · Applied Sciences
  2. The iron melting point determination by 2D simulation
    Shan Yi · 2023 · Journal of Physics Conference Series
  3. Neural network-assisted model of interfacial fluids with explicit coarse-grained molecular structures
    Shuhao Ma, Dechang Li, Xuejin Li, Guoqing Hu · 2024 · The Journal of Chemical Physics
  4. The Integration of Deep Learning and Requirement Analysis in the Design of Recommendation Technology for Blended Learning Platforms
    Changyou Ji · 2024 · Asian Conference on Artificial Intelligence Technology (ACAIT)
  5. WITHDRAWN: Bridging Molecular Simulations and Experimental Observables: Predicting FRET Signals and Conformational Transitions Using MD and Deep Learning in Cas9
    Alejandro G Leyva · 2025 · bioRxiv (Cold Spring Harbor Laboratory)
  6. Bioinformatics Tools: Insights from Structural Approaches
    Nimai Charan Mahanandia, Satyaranjan Biswal, Chirasmita Nayak, Mohammad Samir Farooqi, Sudhir Srivastava, Dwijesh Chandra Mishra, Krishna Kumar Chaturvedi, Anu Sharma · 2025 · Springer eBooks
  7. Machine Learning Applications in Pipeline Integrity Management
    Muhammad Hussain, Tieling Zhang · 2025 · Lecture notes in energy
  8. Active subspace learning for coarse-grained molecular dynamics
    Anna Wojnar, Stephen Pankavich, Alexander J. Pak · 2026 · The Journal of Chemical Physics
  9. Mapping Conformational and Kinetic Landscapes of 14-3-3σ/α-Synuclein Assembly via Variational Autoencoders and Markov State Models
    Gourav Chakraborty, Niladri Patra · 2025 · Journal of Chemical Information and Modeling
  10. Machine Learning Force Fields
    Zhifeng Jing, Yanxing Wang, Chengwen Liu · 2025 · Springer handbooks
  11. Harnessing Artificial Intelligence to Revolutionise Molecular Modelling and Simulations
    Xi Lin, Y. G. Li, Xiaoxuan Yu, Xuhan Liu, Yao-Kun Lei, Yi Yang · 2026 · Springer eBooks
  12. Traj2Relax: A Trajectory-Supervised Method for Robust Structure Relaxation
    Zhiyuan Liu, Quan QIAN · 2026 · Journal of Chemical Information and Modeling
  13. BACH, a Bayesian Optimization Protocol for Accurate Coarse‐Grained Parameterization of Organic Liquids
    Janak Prabhu, Akhil Pratap Singh, Pablo Campomanes, Stefano Vanni · 2026 · Advanced Functional Materials
  14. Simulon: An AI ‐Assisted, PyTorch ‐Native Framework of Molecular Dynamics and Modeling
    Zongxiao Jin, Xiaobo Sun, Xiaoli Xi, Zuoren Nie · 2026 · Journal of Computational Chemistry
  15. Disentangled autoencoding equivariant diffusion model for controlled generation of 3D molecules
    Tianxiao Li, Haoran Liu, Hongyu Guo, Mark Gerstein, Martin Renqiang Min · 2026 · Nature Communications
  16. Temperature dependence of thermal conductivity in amorphous Sb2Te3
    Ao Xu, Kaiqi Li, Jian Zhou, Zhimei Sun · 2026 · Scripta Materialia
  17. Artificial intelligence in migraine: a narrative review on the diagnostic, prognostic, and therapeutic applications
    Mahtasadat Yadollahi, Sahba Azadikhah Jahromi, Sanaz Bordbar, Kimia Kazemzadeh, Abbas Tafakhori · 2026 · Oxford Open Neuroscience
  18. ABRefine: An Accurate Antibody Structure Refinement Method by Equivariant Graph Transformer with Rigid Body Constraint
    Jinxian Wang, Ziqiao Zhang, Xiaoyang Jing, Shuangjia Zheng, Jihong Guan, Shuigeng Zhou · 2026 · Journal of Medicinal Chemistry
  19. Spatiotemporal graph neural networks reveal conformational binding signature in protein dynamics
    Stefano Motta, Gianluca Santini, Samman Mansoor, Ferdoos Hossein Nezhad, Massimiliano Meli, Alessandro Pandini · 2026 · bioRxiv (Cold Spring Harbor Laboratory)
  20. Food-derived antimicrobial peptides: advances in sources, mechanisms, structure-activity relationships, and AI-assisted design
    Yinxiao Zhang, Jiulong An, Wenhui Li, Jun Xi, Kunlun Liu, He Li, Xinqi Liu · 2026 · Food Research International
  21. Model for Parameterization of Interatomic Interaction Potentials by Quantum-Mechanical Descriptors on the Basis of a Graph Neural Network
    A. D. Terets, T.Yu. Nikolayenko · 2026 · Ukrainian Journal of Physics
  22. A Perspective on the Application of Machine Learning and Deep Learning in the Drug Discovery Pipeline
    Khac‐Minh Thai, Cuong Quoc Duong, Nguyen TTT, Van-Thanh Tran, Quang-Minh Mai, Minh-Tri Le · 2026 · Royal Society of Chemistry eBooks
  23. Machine Learning in Molecular Dynamics Applications in Medicinal Chemistry
    Sk. Abdul Amin, Supratik Kar, Stefano Piotto · 2026 · Royal Society of Chemistry eBooks
  24. Differentiable Grand-Canonical Monte Carlo with Importance-Sampled Insertions for Gas Adsorption in Porous Materials
    Egor I. Tuzharov, Yury Maximov, Anastasia V. Bochenkova · 2026 · Journal of Chemical Theory and Computation
  25. TorchFF: A high-performance GPU-accelerated differentiable force field library
    Yingze Wang, Aalim S. Abdullah, Rohith Srinivaas Mohanakrishnan, Teresa L. Head-Gordon · 2026 · The Journal of Chemical Physics
  26. Physics-informed deep learning: A new paradigm for macromolecular recognition and rational protein design
    Haobo Xie, Hao Wang, Xiaojun Yao, Jinyan Li, Yi Pan, Aiping Lü, Xiangxiang Zeng, Dongsheng Cao, Xiangzheng Fu · 2026 · The Innovation Drug Discovery
  27. Multiscale Interpretable Deep-Learning Framework for the Identification and Visualization of Deformation Stages in Molecular Dynamics Trajectory
    Zhengwu Long, Xinyu Wang, Lingyun You · 2026 · Journal of Chemical Information and Modeling
  28. Advances in the Use of Artificial Intelligence in the Pharmaceutical Industry
    Andersson F. Montaña, Jorge A. Mora, José J. Martínez, Daniel Felipe Pulido, María Helena Brijaldo · 2026 · BioMedInformatics
  29. Beyond Energies and Forces: Systematic Evaluation of Machine-Learned Potentials for Solid–Liquid Interfaces
    Nisarg Joshi, R. P. Verma, Jim Pfaendtner · 2026 · ACS Physical Chemistry Au
  30. RNADaemon: Reinforcement Learning-Guided Molecular Dynamics for RNA Tertiary Structure Prediction.
    Wen-Tao Xu, You Wu, Xiang Yu · 2026 · Journal of Chemical Information and Modeling