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
- PREFMoDeL: A Systematic Review and Proposed Taxonomy of Biomolecular Features for Deep LearningJacob L. North, Victor L. Hsu · 2023 · Applied Sciences
- The iron melting point determination by 2D simulationShan Yi · 2023 · Journal of Physics Conference Series
- Neural network-assisted model of interfacial fluids with explicit coarse-grained molecular structuresShuhao Ma, Dechang Li, Xuejin Li, Guoqing Hu · 2024 · The Journal of Chemical Physics
- The Integration of Deep Learning and Requirement Analysis in the Design of Recommendation Technology for Blended Learning PlatformsChangyou Ji · 2024 · Asian Conference on Artificial Intelligence Technology (ACAIT)
- WITHDRAWN: Bridging Molecular Simulations and Experimental Observables: Predicting FRET Signals and Conformational Transitions Using MD and Deep Learning in Cas9Alejandro G Leyva · 2025 · bioRxiv (Cold Spring Harbor Laboratory)
- Bioinformatics Tools: Insights from Structural ApproachesNimai Charan Mahanandia, Satyaranjan Biswal, Chirasmita Nayak, Mohammad Samir Farooqi, Sudhir Srivastava, Dwijesh Chandra Mishra, Krishna Kumar Chaturvedi, Anu Sharma · 2025 · Springer eBooks
- Machine Learning Applications in Pipeline Integrity ManagementMuhammad Hussain, Tieling Zhang · 2025 · Lecture notes in energy
- Active subspace learning for coarse-grained molecular dynamicsAnna Wojnar, Stephen Pankavich, Alexander J. Pak · 2026 · The Journal of Chemical Physics
- Mapping Conformational and Kinetic Landscapes of 14-3-3σ/α-Synuclein Assembly via Variational Autoencoders and Markov State ModelsGourav Chakraborty, Niladri Patra · 2025 · Journal of Chemical Information and Modeling
- Machine Learning Force FieldsZhifeng Jing, Yanxing Wang, Chengwen Liu · 2025 · Springer handbooks
- Harnessing Artificial Intelligence to Revolutionise Molecular Modelling and SimulationsXi Lin, Y. G. Li, Xiaoxuan Yu, Xuhan Liu, Yao-Kun Lei, Yi Yang · 2026 · Springer eBooks
- Traj2Relax: A Trajectory-Supervised Method for Robust Structure RelaxationZhiyuan Liu, Quan QIAN · 2026 · Journal of Chemical Information and Modeling
- BACH, a Bayesian Optimization Protocol for Accurate Coarse‐Grained Parameterization of Organic LiquidsJanak Prabhu, Akhil Pratap Singh, Pablo Campomanes, Stefano Vanni · 2026 · Advanced Functional Materials
- Simulon: An AI ‐Assisted, PyTorch ‐Native Framework of Molecular Dynamics and ModelingZongxiao Jin, Xiaobo Sun, Xiaoli Xi, Zuoren Nie · 2026 · Journal of Computational Chemistry
- Disentangled autoencoding equivariant diffusion model for controlled generation of 3D moleculesTianxiao Li, Haoran Liu, Hongyu Guo, Mark Gerstein, Martin Renqiang Min · 2026 · Nature Communications
- Temperature dependence of thermal conductivity in amorphous Sb2Te3Ao Xu, Kaiqi Li, Jian Zhou, Zhimei Sun · 2026 · Scripta Materialia
- Artificial intelligence in migraine: a narrative review on the diagnostic, prognostic, and therapeutic applicationsMahtasadat Yadollahi, Sahba Azadikhah Jahromi, Sanaz Bordbar, Kimia Kazemzadeh, Abbas Tafakhori · 2026 · Oxford Open Neuroscience
- ABRefine: An Accurate Antibody Structure Refinement Method by Equivariant Graph Transformer with Rigid Body ConstraintJinxian Wang, Ziqiao Zhang, Xiaoyang Jing, Shuangjia Zheng, Jihong Guan, Shuigeng Zhou · 2026 · Journal of Medicinal Chemistry
- Spatiotemporal graph neural networks reveal conformational binding signature in protein dynamicsStefano Motta, Gianluca Santini, Samman Mansoor, Ferdoos Hossein Nezhad, Massimiliano Meli, Alessandro Pandini · 2026 · bioRxiv (Cold Spring Harbor Laboratory)
- Food-derived antimicrobial peptides: advances in sources, mechanisms, structure-activity relationships, and AI-assisted designYinxiao Zhang, Jiulong An, Wenhui Li, Jun Xi, Kunlun Liu, He Li, Xinqi Liu · 2026 · Food Research International
- Model for Parameterization of Interatomic Interaction Potentials by Quantum-Mechanical Descriptors on the Basis of a Graph Neural NetworkA. D. Terets, T.Yu. Nikolayenko · 2026 · Ukrainian Journal of Physics
- A Perspective on the Application of Machine Learning and Deep Learning in the Drug Discovery PipelineKhac‐Minh Thai, Cuong Quoc Duong, Nguyen TTT, Van-Thanh Tran, Quang-Minh Mai, Minh-Tri Le · 2026 · Royal Society of Chemistry eBooks
- Machine Learning in Molecular Dynamics Applications in Medicinal ChemistrySk. Abdul Amin, Supratik Kar, Stefano Piotto · 2026 · Royal Society of Chemistry eBooks
- Differentiable Grand-Canonical Monte Carlo with Importance-Sampled Insertions for Gas Adsorption in Porous MaterialsEgor I. Tuzharov, Yury Maximov, Anastasia V. Bochenkova · 2026 · Journal of Chemical Theory and Computation
- TorchFF: A high-performance GPU-accelerated differentiable force field libraryYingze Wang, Aalim S. Abdullah, Rohith Srinivaas Mohanakrishnan, Teresa L. Head-Gordon · 2026 · The Journal of Chemical Physics
- Physics-informed deep learning: A new paradigm for macromolecular recognition and rational protein designHaobo Xie, Hao Wang, Xiaojun Yao, Jinyan Li, Yi Pan, Aiping Lü, Xiangxiang Zeng, Dongsheng Cao, Xiangzheng Fu · 2026 · The Innovation Drug Discovery
- Multiscale Interpretable Deep-Learning Framework for the Identification and Visualization of Deformation Stages in Molecular Dynamics TrajectoryZhengwu Long, Xinyu Wang, Lingyun You · 2026 · Journal of Chemical Information and Modeling
- Advances in the Use of Artificial Intelligence in the Pharmaceutical IndustryAndersson F. Montaña, Jorge A. Mora, José J. Martínez, Daniel Felipe Pulido, María Helena Brijaldo · 2026 · BioMedInformatics
- Beyond Energies and Forces: Systematic Evaluation of Machine-Learned Potentials for Solid–Liquid InterfacesNisarg Joshi, R. P. Verma, Jim Pfaendtner · 2026 · ACS Physical Chemistry Au
- 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