Machine learning approaches for analyzing and enhancing molecular dynamics simulations

Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling

Torrie, G. M., & Valleau, J. P. (1977) · Journal of Computational Physics · doi:10.1016/0021-9991(77)90121-8 · Cited by 6,091

Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces

Behler, J., & Parrinello, M. (2007) · Physical Review Letters · doi:10.1103/physrevlett.98.146401 · Cited by 5,010

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

Bartók, A. P., Payne, M. C., Kondor, R., & Csányi, G. (2010) · Physical Review Letters · doi:10.1103/physrevlett.104.136403 · Cited by 3,242

Density estimation using Real NVP

Dinh, L., Sohl-Dickstein, J., & Bengio, S. (2016) · arXiv · Cited by 2,067

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

Smith, J. S., Isayev, O., & Roitberg, A. E. (2017) · Chemical Science · doi:10.1039/c6sc05720a · Cited by 1,921

Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps

Coifman, R. R., Lafon, S., Lee, A. B., Maggioni, M., Nadler, B., Warner, F., & Zucker, S. W. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0500334102 · Cited by 1,675

Promoting transparency and reproducibility in enhanced molecular simulations

Bonomi, M., Bussi, G., Bernetti, M., Cesari, A., Laio, A., Camilloni, C., Paissoni, C., Tribello, G. A., Banáš, P., Barducci, A., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0506-8 · Cited by 1,193

PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models

Scherer, M. K., Trendelkamp-Schroer, B., Paul, F., Pérez‐Hernández, G., Hoffmann, M., Plattner, N., Wehmeyer, C., Prinz, J.-H., & Noé, F. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00743 · Cited by 1,160

Identification of slow molecular order parameters for Markov model construction

Pérez-Hernández, G., Paul, F., Giorgino, T., De Fabritiis, G., & Noé, F. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4811489 · Cited by 990 · cited 2×

Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning

Noé, F., Olsson, S., Köhler, J., & Wu, H. (2019) · Science · doi:10.1126/science.aaw1147 · Cited by 863

Using graph neural network and symbolic regression to model disordered systems

Chen, R., Bauchy, M., Wang, W., Sun, Y., Tao, X., & Marian, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-05205-8 · Cited by 4

Combining enhanced sampling and deep learning dimensionality reduction for the study of the heat shock protein B8 and its pathological mutant K141E

Montepietra, D., Cecconi, C., & Brancolini, G. (2022) · RSC Advances · doi:10.1039/d2ra04913a · Cited by 4

Molecular Modeling in Drug Delivery: Polymer Protective Coatings as Case Study

Bunker, A., & Kehrein, J. (2024) · Wiley eBooks · doi:10.1002/9781119987260.ch5 · Cited by 5

Unveiling the influence of fastest nobel prize winner discovery: alphafold’s algorithmic intelligence in medical sciences

Najafi, N. N., Karbassian, R., Hajihassani, H., & Irani, M. A. (2025) · Journal of Molecular Modeling · doi:10.1007/s00894-025-06392-x · Cited by 4

Deformation mechanisms of multiphase AlCoCuFeNiTi high-entropy alloys revealed by machine learning-assisted molecular dynamics

Lu, Y.-S., Yamanaka, K., Ishii, H., Bui, T.-X., Hsieh, Y.-J., & Fang, T. (2025) · Materials Today Physics · doi:10.1016/j.mtphys.2025.101900 · Cited by 3

Protein–DNA interactions in disease and drug discovery

Mishra, R., & Ahmad, S. (2025) · Chemical Communications · doi:10.1039/d5cc06239j · Cited by 5

Advancements in characterization of protein dynamics with machine learning

Madani, M., & Tarakanova, A. (2026) · npj Soft Matter · doi:10.1038/s44431-026-00025-w · Cited by 3

Quasi-Classical Trajectory Calculation of Rate Constants Using an Ab Initio Trained Machine Learning Model (aML-MD) with Multifidelity Data

Shi, Z., Lele, A. D., Jasper, A. W., Klippenstein, S. J., & Ju, Y. (2024) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.4c00750 · Cited by 3

Screening Fast-Mode Motion in Collective Variable Discovery for Biochemical Processes.

Shao, D., Zhang, Z., Liu, X., Fu, H.-H., Shao, X.-G., & Cai, W.-S. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01282 · Cited by 3

Applications of Molecular Dynamics Simulation and MM-PBSA Methods in Discovery of Veterinary Drugs

Kumar, N., Mandal, P., Kumar, B., Rani, P., & Singh, D. V. (2025) · Springer eBooks · doi:10.1007/978-981-97-7395-4_14 · Cited by 3

First-passage approach to optimizing perturbations for improved training of machine learning models

Meir, S., Keidar, T. D., Reuveni, S., & Hirshberg, B. (2025) · Machine Learning Science and Technology · doi:10.1088/2632-2153/add8df · Cited by 9

Highly Efficient Biosynthesis of O-(3-Chloro-2-propenyl)hydroxylamine through Structure-Guided Engineering of Deacetylase

Huang, Y., Li, X., Han, M., Huang, J.-H., Li, L., Zou, S., Xue, Y., & Zheng, Y. (2025) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.5c03751 · Cited by 3

Clustering and Analyzing Ensembles of Residue Interaction Networks from Molecular Dynamics Simulations

Franke, L., & Peter, C. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01298 · Cited by 3

A computational evaluation of FDA medicines’ ability to inhibit hypoxia-inducible factor prolyl hydroxylase-2 (PHD-2) for acute respiratory distress syndrome

Chandrasekaran, J., & Balasubramaniam, J. (2022) · Structural Chemistry · doi:10.1007/s11224-022-02012-z · Cited by 3

Optimized reaction coordinates for analysis of enhanced sampling

Widmer, J., Langini, C., Vitalis, A., & Caflisch, A. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0149207 · Cited by 4

Activating mutations drive human MEK1 kinase using a gear-shifting mechanism

Patil, K., Wang, Y., Chen, Z., Suresh, K., & Radhakrishnan, R. (2023) · Biochemical Journal · doi:10.1042/bcj20230281 · Cited by 4

Machine learning in molecular simulations of biomolecules

Guan, X., Huang, H.-Y., Peng, H.-Q., Liu, Y.-H., Li, W., & Wang, W. (2023) · Acta Physica Sinica · doi:10.7498/aps.72.20231624 · Cited by 3

A 3d convolutional neural network autoencoder for predicting solvent configuration changes in condensed phase biomass reactions

Puliyanda, A., Padmanathan, A. M. D., Mushrif, S. H., & Prasad, V. (2024) · Digital Discovery · doi:10.1039/d4dd00049h · Cited by 3

Assessment of machine learning models trained by molecular dynamics simulations results for inferring ethanol adsorption on an aluminium surface

Shahbazi, F., Esfahani, M. N., Keshmiri, A., & Jabbari, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-71007-z · Cited by 4

Improved Data-Driven Collective Variables for Biased Sampling through Iteration on Biased Data

Sasmal, S., McCullagh, M., & Hocky, G. M. (2025) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.5c02164 · Cited by 3

Learning to Make Chemical Predictions: the Interplay of Feature Representation, Data, and Machine Learning AlgorithmsPreprint

Haghighatlari, M., Li, J., Heidar‐Zadeh, F., Liu, Y., Guan, X., & Head-Gordon, T. L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.00157 · Cited by 3

MOLECULAR MODELING OF HIGH-PERFORMANCE THERMOSET POLYMER MATRIX COMPOSITES FOR AEROSPACE APPLICATIONS

Deshpande, P. (2022) · doi:10.37099/mtu.dc.etdr/1430 · Cited by 3

Improving the Efficiency of Variationally Enhanced Sampling with Wavelet-Based Bias Potentials

Pampel, B., & Valsson, Ó. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00197 · Cited by 10

Redox Mediators for Li2CO3 Decomposition

Liu, Z., Huang, H., Chen, Z., He, H., Wang, D., & Li, Z. P. (2025) · Inorganics · doi:10.3390/inorganics13060192 · Cited by 2

Design, synthesis, in silico , anticancer evaluations of N -acylhydrazone derivatives as STAT3 inhibitors

Panwar, V., Rohila, Y., Singh, A. P., Meenakshi, M., Singh, B., Gupta, M. K., Garg, M., & Kumar, D. (2025) · Future Medicinal Chemistry · doi:10.1080/17568919.2025.2527604 · Cited by 2

Understanding Mechanisms of Molecular Rare Events from Start to Finish

Breebaart, R. S., Lazzeri, G., Covino, R., & Bolhuis, P. G. (2026) · Physical Review Letters · doi:10.1103/lk32-njx7 · Cited by 2

Deep learning insights into β-lactamase dynamics and resistance evolution

Gu, J., Gao, L., Chen, S., Xu, M., Goyal, J., Haider, R., Attana, F., Alzahrani, O. R., Bonomo, R. A., & Haider, S. M. (2026) · Biochemical Journal · doi:10.1042/bcj20260196 · Cited by 2

Guide for determination of protein structural ensembles by combining cryo‐ EM data with metadynamics

Brotzakis, Z. F. (2022) · FEBS Open Bio · doi:10.1002/2211-5463.13542 · Cited by 2

Machine learning for in silico protein research

Zhang, J. (2024) · Acta Physica Sinica · doi:10.7498/aps.73.20231618 · Cited by 2

Helical reorganization in the context of membrane protein folding: Insights from simulations with bacteriorhodopsin (BR) fragments

Chatterjee, H., Jaysingh, M. A., Zacharias, M., & Sengupta, N. (2024) · Biochimica et Biophysica Acta (BBA) - Biomembranes · doi:10.1016/j.bbamem.2024.184333 · Cited by 2

Molecular Modeling Is Key to Understanding Supramolecular Resorcinarenyl Capsules, Inclusion Complex Formation and Organic Reactions in Nanoconfined Space

Steinmetz, M., & Sémeril, D. (2025) · Molecules · doi:10.3390/molecules30122549 · Cited by 2

Machine Learning in Molecular Dynamics Simulations of Biomolecular Systems

Kolloff, C., & Olsson, S. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-821978-2.00089-1 · Cited by 7

DiffNets: Self-supervised deep learning to identify the mechanistic basis for biochemical differences between protein variantsPreprint

Ward, M. D., Zimmerman, M. I., Meller, A., Chung, M., Swamidass, S. J., & Bowman, G. (2020) · bioRxiv · doi:10.1101/2020.07.01.182725 · Cited by 1

Study of microstructural growth under cyclic martensite phase transition in shape memory alloys; A molecular dynamics approach

Moravej, S. A., Taghibakhshi, A., Pishkenari, H. N., & Arghavani, J. (2021) · Journal of Intelligent Material Systems and Structures · doi:10.1177/1045389x211023972 · Cited by 1

Quasi-classical Trajectory Calculation of Rate Constants Using Ab-initio Trained Machine Learning Force Field (aML-MD)

Shi, Z., Lele, A. D., Jasper, A. W., Klippenstein, S. J., & Ju, Y. (2024) · AIAA SciTech Forum · doi:10.2514/6.2024-0797 · Cited by 1

Computational modeling of intrinsically disordered and phase-separated protein states

Coskuner‐Weber, O., & Uversky, V. N. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-21809-5.00015-6 · Cited by 1

Accelerating molecular dynamics simulations with artificial intelligence

Qian, R., Yang, S., Song, Z., & Huang, J. (2025) · Scientia Sinica Chimica · doi:10.1360/ssc-2025-0055 · Cited by 1

The key role of the dynamics and flexibility of proteins in functional mechanisms: How computational methods can contribute to their identification

Lin, R. L. F., Bellaiche, A., & Etchebest, C. (2025) · Biochimie · doi:10.1016/j.biochi.2025.10.007 · Cited by 1

A model for shale gas flow and permeability leveraging coupled transport mechanisms

Chen, S., Liu, F., Wen, F.-G., Duan, H., & Peng, X.-L. (2026) · Petroleum Science · doi:10.1016/j.petsci.2026.03.013 · Cited by 1

Computational techniques to model nanomaterials

Pakzad, S. Z., & Esfahani, M. N. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-28820-3.00007-x · Cited by 1

From molecules to structures: Role of artificial intelligence in fatigue fracture characterization of concrete

Gupta, M., & Bhowmik, S. (2026) · Journal of Building Engineering · doi:10.1016/j.jobe.2026.115919 · Cited by 1

Classical and machine learning driven molecular dynamics in battery research

Oh, Y., & Bae, J. (2026) · Current Applied Physics · doi:10.1016/j.cap.2026.03.013 · Cited by 1

Wavelet-Enhanced Data-Driven Collective Variables for Efficient Sampling of Protein Folding Landscapes

Zhang, Z., Shao, X.-G., & Cai, W.-S. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c00310 · Cited by 1

Impact of native oxide on the vibrational response of silicon nanowires

Pakzad, S. Z., Gesing, A. L., Schmid, U., Schneider, M., & Alaca, B. E. (2026) · Mechanics of Advanced Materials and Structures · doi:10.1080/15376494.2026.2665810 · Cited by 1

Thermally activated snap-through transitions controlled by tunable free energy landscape

Zhao, R., Zhang, Y., Luo, C., & Wang, Y. (2026) · The Journal of Chemical Physics · doi:10.1063/5.0327295 · Cited by 1

Reimagining road pavements: Visionary approaches to future functionality and design

Sreeram, A., Palin, D., Hajj, R., Xu, X., Wang, H., Ashish, P. K., Hu, Y., & Saxena, A. (2026) · Journal of Traffic and Transportation Engineering (English Edition) · doi:10.1016/j.jtte.2025.09.006 · Cited by 1

Machine learning–assisted structural characterization of ring size distribution in Sr-doped bioactive glasses: A molecular dynamics study

Moghanian, A., & Baino, F. (2026) · Ceramics International · doi:10.1016/j.ceramint.2026.06.158 · Cited by 1

AI‐Physics‐Experiment Trinity for Integrated Protein Dynamics Modeling

Chen, S. T., Low, M., Xiu, P., Lindorff‐Larsen, K., & Wang, Y. (2026) · Advanced Science · doi:10.1002/advs.76023 · Cited by 1

AI-Driven Protein Research: From Prediction to Design

Min, G., & Peng, H. (2026) · Methods in molecular biology · doi:10.1007/978-1-0716-5249-7_13 · Cited by 1

PETIMOT : a novel framework for inferring protein motions from sparse data using SE(3)-equivariant graph neural networks

Lombard, V., Van, J. N., Grudinin, S., & Laine, É. (2026) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s2059798326006054 · Cited by 4

Online Boosted Gaussian Learners for In-Situ Detection and Characterization of Protein Folding States in Molecular Dynamics Simulations

Sahni, H., Carrillo-Cabada, H., Kots, E. D., Caíno‐Lores, S., Marquez, J. D., Deelman, E., Cuendet, M. A., Weinstein, H., Taufer, M., & Estrada, T. (2023) · IEEE International Conference on eScience · doi:10.1109/e-science58273.2023.10254895 · Cited by 1

State of the Art and Outlook of Data Science and Machine Learning in Organic Chemistry

Stefani, R. (2023) · Current Organic Chemistry · doi:10.2174/0113852728249020230921072236 · Cited by 2

Harness the power of atomistic modeling and deep learning in biofuel separation

Zhang, D., Wu, H., Smith, B. A., & Glezakou, V. (2023) · Annual reports in computational chemistry · doi:10.1016/bs.arcc.2023.10.001 · Cited by 1

FPGA-Based Numerical Simulation of the Chaotic Synchronization of Chua Circuits

Rentería, L., Mayacela, M., Rodriguez, K. H. T., Ramírez, W., Real, R. B. D., & Acosta, R. (2024) · Computation · doi:10.3390/computation12090174 · Cited by 1

Increasing the Efficiency of Ensemble Molecular Dynamics Simulations with Termination of Unproductive Trajectories Identified at Runtime

Marquez, J. D., Cuendet, M., Caíno-Lores, S., Estrada, T., Deelman, E., Weinstein, H., & Taufer, M. (2025) · Journal of Physical Chemistry A · doi:10.1021/acs.jpca.4c05182

A theoretical study of oxygen anion diffusion through La and Ca doped SrTiO 3 lattice structures: molecular dynamics and ML approaches

Sharma, K. D., Gade, H. M., & Kumari, N. (2025) · Physical Chemistry Chemical Physics · doi:10.1039/d4cp04657a · Cited by 1

Predicting Antibody–Antigen Interactions with Structure-Aware LLMs: Insights from SARS-CoV-2 Variants

Ashraf, F. B., Madrigal, V. A., & Lonardi, S. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00973 · Cited by 1

Discovering loop conformational flexibility in T4 lysozyme mutants through artificial intelligence aided molecular dynamicsPreprint

Smith, Z., Ravindra, P., Wang, Y., Cooley, R., & Tiwary, P. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.08.032748 · Cited by 1

Information theoretics for the machine learning detection of functionally conserved and coordinated protein motionsPreprint

Babbitt, G. A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.29.089003

On Performance Enhancement of Molecular Dynamics Simulation Using HPC Systems

Rathod, T., Shah, M., Shah, N. K., Raval, G., Bhavsar, M. D., & Ganesh, R. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-0733-2_73

Multi-Agent Reinforcement Learning-based Adaptive Sampling for Conformational Sampling of ProteinsPreprint

Kleiman, D. E., & Shukla, D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.31.494208

Review: Challenges and Frontiers of Computational Modeling of Biomolecular Recognition — R0/PR1

Wang, J., Bhattarai, A., Do, H. N., et al. (2022) · QRB Discovery · doi:10.1017/qrd.2022.11.pr1

Review: Challenges and Frontiers of Computational Modeling of Biomolecular Recognition — R0/PR2

Wang, J., Bhattarai, A., Do, H. N., et al. (2022) · QRB Discovery · doi:10.1017/qrd.2022.11.pr2

Recommendation: Challenges and Frontiers of Computational Modeling of Biomolecular Recognition — R1/PR4

Wang, J., Bhattarai, A., Do, H. N., et al. (2022) · QRB Discovery · doi:10.1017/qrd.2022.11.pr4

Recommendation: Challenges and Frontiers of Computational Modeling of Biomolecular Recognition — R0/PR3

Wang, J., Bhattarai, A., Do, H. N., et al. (2022) · QRB Discovery · doi:10.1017/qrd.2022.11.pr3

Recommendation: Computational prediction of ω-transaminase selectivity by deep learning analysis of molecular dynamics trajectories — R0/PR3

Ramírez-Palacios, C., & Marrink, ‪. J. (2022) · QRB Discovery · doi:10.1017/qrd.2022.22.pr3

Review: Computational prediction of ω-transaminase selectivity by deep learning analysis of molecular dynamics trajectories — R0/PR2

Ramírez-Palacios, C., & Marrink, ‪. J. (2022) · QRB Discovery · doi:10.1017/qrd.2022.22.pr2

Recommendation: Computational prediction of ω-transaminase selectivity by deep learning analysis of molecular dynamics trajectories — R2/PR5

Ramírez-Palacios, C., & Marrink, ‪. J. (2022) · QRB Discovery · doi:10.1017/qrd.2022.22.pr5

Recommendation: Computational prediction of ω-transaminase selectivity by deep learning analysis of molecular dynamics trajectories — R1/PR4

Ramírez-Palacios, C., & Marrink, ‪. J. (2022) · QRB Discovery · doi:10.1017/qrd.2022.22.pr4

Review: Computational prediction of ω-transaminase selectivity by deep learning analysis of molecular dynamics trajectories — R0/PR1

Ramírez-Palacios, C., & Marrink, ‪. J. (2022) · QRB Discovery · doi:10.1017/qrd.2022.22.pr1

PREFMoDeL: A Systematic Review and Proposed Taxonomy of Biomolecular Features for Deep Learning

North, J. L., & Hsu, V. L. (2023) · Applied Sciences · doi:10.3390/app13074356

Identification of InhA-Inhibitors Interaction Fingerprints that Affect Residence Time

Ługowska, M., & Pacholczyk, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-34953-9_2

Review of “Integrated Computer-Aided Process Engineering Session in the International Symposium on Innovation in Materials Processing (ISIMP, 26–29 October 2021)”

Choi, H., Kim, J., Cha, P., & Kim, H. S. (2023) · MATERIALS TRANSACTIONS · doi:10.2320/matertrans.mt-m2023087

Hamiltonian Neural Network 6-DoF Rigid-Body Dynamic Modeling Based on Energy Variation Estimation

Fei, S., Lin, H., Sun, Z., He, W., Yuanjie, L., Jile, H., Qing, L., & Qihang, S. (2023) · Complexity · doi:10.1155/2023/8882781

Alkaloids Lead to Potential Inhibition of the Acyl Carrier Protein Reductase to Attenuate Tuberculosis; an in-silico Analysis

Kamran, P., & Ibrahim, A. (2023) · Biology Medicine & Natural Product Chemistry · doi:10.14421/biomedich.2023.122.441-450

Structure-based virtual screening of natural compounds as inhibitors of HCV using molecular docking and molecular dynamics simulation studies

Sabei, F. Y., Safhi, A. Y., Almoshari, Y., Salawi, A., Sultan, M. H., Bakkari, M. A., Alsalhi, A., Madkhali, O. A. A., Jali, A. M., & Ahsan, W. (2023) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2023.2263588

Information Thermodynamics of Transition Paths

Louwerse, M. (2023) · Springer theses · doi:10.1007/978-3-031-40534-1_3

Variational analysis and AI algorithm implementation of free energy landscapes of molecular system

Du, B., & Tian, P. (2024) · Acta Physica Sinica · doi:10.7498/aps.73.20231800

Machine Learning of Slow Collective Variables and Enhanced Sampling via Spatial TechniquesPreprint

Gökdemir, T., & Rydzewski, J. (2025) · Qeios · doi:10.32388/70q2ov

The Influence of Functional Modification on Wear Resistance of Graphene/Nbr Composites: Combined Molecular Dynamics and Interpretable Machine LearningPreprint

YUAN, Q., Li, Y., Cui, J., He, E., & Wang, S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5252358

The Influence of Functional Modification on Wear Resistance of Graphene/Nbr Composites: Combined Molecular Dynamics and Interpretable Machine LearningPreprint

YUAN, Q., Li, Y., Cui, J., He, E., & Wang, S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5269440

Time-series prediction of velocity trajectories using generative AI for estimating molecular properties

Tanaka, S., & Shibahara, M. (2025) · Molecular Simulation · doi:10.1080/08927022.2025.2523956

Designing a Multi-Epitope Vaccine Candidate by Targeting Viral Infections Associated with Multiple Sclerosis Via Immunoinformatics ApproachesPreprint

Alijanizadeh, P., Saleki, K., & Ferdosi-Shahandashti, E. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5339358

Special Issue: “Advanced Research on Molecular Modeling of Protein Structure and Functions”

Khrenova, M. G. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26167916

Ab Initio -Trained Machine Learning Molecular Dynamics Model for Radical Reactions in Hydrogen Combustion

Shi, Z., Lele, A. D., Jasper, A. W., Klippenstein, S. J., & Ju, Y. (2026) · AIAA SciTech Forum · doi:10.2514/6.2026-1623

DynMoCo: A novel AI framework to reveal modular substructures of protein from molecular dynamics

Mao, L., Kwak, M., Ashkezari, A. H. K., Li, Z., Cong, P., Chen, Y., Phee, J. H., Kang, S., Li, J., & Zhu, C. (2026) · Biophysical Journal · doi:10.1016/j.bpj.2026.03.034

Deep learning of committor and explainable artificial intelligence analysis for identifying reaction coordinates

Mori, T., Okazaki, K.-I., Kim, K., & Matubayasi, N. (2026) · Chemical Physics Reviews · doi:10.1063/5.0335684

Lost in Projection? Gaussian Filtering Recovers Hidden Conformational States

Sartore, S., Nagel, D., Diez, G., & Stock, G. (2026) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.6c00341 · Cited by 1

Molecular Dynamics Exploration of Two Full-Length IgG Antibodies: Impact of GlycosylationPreprint

Lin, R. L. F., Bellaiche, A., Diharce, J., & Etchebest, C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.14.718417

From Enhanced Sampling to Human-Readable Representations of Protein Dynamics

Mondal, S., Sauer, M. A., & Heyden, M. (2026) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6c00914

The Future of Polymer Science: Artificial Intelligence and Beyond

Akshita, Shukla, D., Verma, R., Gupta, T. K., Gupta, D., Choubey, R. K., Kumar, S., & Kumar, V. (2026) · Engineering materials · doi:10.1007/978-981-95-8353-9_10

Educational and Training Tools for AI in Polymer Science

Bhandari, M., Kathayat, N., Sharma, N. P., Bhatt, P., Melkani, D. C., Dewali, S., Swetapadma, D., & Bisht, S. S. (2026) · Engineering materials · doi:10.1007/978-981-95-8353-9_11

Artificial Intelligence-driven mechanistic insights in electrocatalysis

Kim, J., Woo, S., Jung, H., Kim, J., Mun, J., & Kim, J. H. (2026) · Applied Catalysis B: Environmental · doi:10.1016/j.apcatb.2026.127014

Allosteric crosstalk between Fc glycans and Fab regions in full-length IgG2 and IgG4 antibodiesPreprint

Etchebest, C., Lin, R. L. F., Bellaiche, A., & Diharce, J. (2026) · Research Square · doi:10.21203/rs.3.rs-9682477/v1

Information-Geometric Audits of Macromolecular Ensembles: A Prescriptive Protocol for Latent Dimension SelectionPreprint

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