Designing green chemicals by predicting vaporization properties using explainable graph attention networks

Yeonjoon Kim, Jaeyoung Cho, Hojin Jung, Lydia E. Meyer, Gina M. Fioroni, Christopher D. Stubbs, Keunhong Jeong, Robert L. McCormick, Peter C. St. John and Seonah Kim

Green Chem., 2024, 26, 10247
DOI: 10.1039/D4GC01994F

All-atom molecular dynamics simulations of polymer and polyelectrolyte brushes

Raashiq Ishraaq and Siddhartha Das

Chem. Commun., 2024, 60, 6093
DOI: 10.1039/D4CC01557F

The use of collective variables and enhanced sampling in the simulations of existing and emerging microporous materials

Konstantin Stracke and Jack D. Evans

Nanoscale, 2024, 16, 9186
DOI: 10.1039/D4NR01024H

Coupling causality and interpretable machine learning to reveal the reaction coordinate of C–N coupling with a supramolecular Cu-calix[8]arene catalyst

R. A. Talmazan, J. Gamper, I. Castillo, T. S. Hofer and M. Podewitz

Digital Discovery, 2025, 4, 2954
DOI: 10.1039/D5DD00216H

Molecular dynamics-driven drug discovery

Dengjie Yan, Yue Ma, Xiang Chen, Shi Deng and Qiantao Wang

Phys. Chem. Chem. Phys., 2025, 27, 12633
DOI: 10.1039/D5CP00380F

Trapping a non-cognate nucleotide upon initial binding for replication fidelity control in SARS-CoV-2 RNA dependent RNA polymerase

Moises E. Romero, Shannon J. McElhenney and Jin Yu

Phys. Chem. Chem. Phys., 2024, 26, 1792
DOI: 10.1039/D3CP04410F

Lignin-based porous carbon adsorbents for CO2 capture

Daniel Barker-Rothschild, Jingqian Chen, Zhangmin Wan, Scott Renneckar, Ingo Burgert, Yong Ding, Yi Lu and Orlando J. Rojas

Chem. Soc. Rev., 2025, 54, 623
DOI: 10.1039/D4CS00923A

Operando Modeling of Zeolite-Catalyzed Reactions Using First-Principles Molecular Dynamics Simulations

Veronique Van Speybroeck, Massimo Bocus, Pieter Cnudde and Louis Vanduyfhuys

ACS Catal., 2023, 13, 11455
DOI: 10.1021/acscatal.3c01945

Perspectives on Ligand/Protein Binding Kinetics Simulations: Force Fields, Machine Learning, Sampling, and User-Friendliness

Paolo Conflitti, Stefano Raniolo and Vittorio Limongelli

J. Chem. Theory Comput., 2023, 19, 6047
DOI: 10.1021/acs.jctc.3c00641

Enhanced sampling strategies for molecular simulation of DNA

Bernadette Mohr, Thor van Heesch, Alberto Pérez de Alba Ortíz and Jocelyne Vreede

WIREs Comput Mol Sci, 2024, 14
DOI: 10.1002/wcms.1712

AlphaFold-SFA: Accelerated sampling of cryptic pocket opening, protein-ligand binding and allostery by AlphaFold, slow feature analysis and metadynamics

Shray Vats, Raitis Bobrovs, Pär Söderhjelm, Soumendranath Bhakat and Xiakun Chu

PLoS ONE, 2024, 19, e0307226
DOI: 10.1371/journal.pone.0307226

Principal Component Analysis of Molecular Dynamic Trajectories: Concepts, Tools, and Applications

Danilo Roccatano

WIREs Comput Mol Sci, 2025, 15
DOI: 10.1002/wcms.70060

Revealing the Atomistic Mechanism of Rare Events in Molecular Dynamics

Jakob J. Kresse, Alexander Sikorski, Vikram Sunkara, Surahit Chewle and Marcus Weber

J. Chem. Theory Comput., 2026, 22, 2380
DOI: 10.1021/acs.jctc.5c01906

Computational Methods for Modeling Lipid-Mediated Active Pharmaceutical Ingredient Delivery

Markéta Paloncýová, Mariana Valério, Ricardo Nascimento Dos Santos, Petra Kührová, Martin Šrejber, Petra Čechová, Dimitar A. Dobchev, Akshay Balsubramani, Pavel Banáš, Vikram Agarwal, Paulo C. T. Souza and Michal Otyepka

Mol. Pharmaceutics, 2025, 22, 1110
DOI: 10.1021/acs.molpharmaceut.4c00744

Foundations of molecular dynamics simulations: how and what

Giovanni Ciccotti, Sergio Decherchi and Simone Meloni

Riv. Nuovo Cim., 2025, 48, 1
DOI: 10.1007/s40766-025-00065-4

Unsupervised Learning of Progress Coordinates during Weighted Ensemble Simulations: Application to NTL9 Protein Folding

Jeremy M. G. Leung, Nicolas C. Frazee, Alexander Brace, Anthony T. Bogetti, Arvind Ramanathan and Lillian T. Chong

J. Chem. Theory Comput., 2025, 21, 3691
DOI: 10.1021/acs.jctc.4c01136

Machine learning heralding a new development phase in molecular dynamics simulations

Eva Prašnikar, Martin Ljubič, Andrej Perdih and Jure Borišek

Artif Intell Rev, 2024, 57
DOI: 10.1007/s10462-024-10731-4

Integration of topological data analysis and entropy features for the prediction of lithium-ion battery state-of-health

Manoj K. Singh, Anuj Kumar, Sangeeta Pant, Shshank Chaube, Kriti Misra, Jitendra Pal Singh and Ketan Kotecha

Future Batteries, 2025, 6, 100059
DOI: 10.1016/j.fub.2025.100059

Recent Advances in Computer-Aided Structure-Based Drug Design on Ion Channels

Palina Pliushcheuskaya and Georg Künze

IJMS, 2023, 24, 9226
DOI: 10.3390/ijms24119226

Covalent: Interpretable and Discriminative Collective Variables Reveal Ligand-Dependent Switching in Human Cellular Retinol-Binding Protein 2

Myongin Oh, Changin Oh and Eshra Tabassum

J. Chem. Theory Comput., 2025, 21, 12356
DOI: 10.1021/acs.jctc.5c01402

Network pharmacology, docking, and molecular dynamics analysis of Sanhuang decoction in diabetic foot ulcers

Bo Wu, Xiaohong Lan, Yang Yang and Yuekun Wang

Computer Methods in Biomechanics and Biomedical Engineering, 2025, 1
DOI: 10.1080/10255842.2025.2506798

Advances in the Simulations of Enzyme Reactivity in the Dawn of the Artificial Intelligence Age

Katarzyna Świderek, Joan Bertran, Kirill Zinovjev, Iñaki Tuñón and Vicent Moliner

WIREs Comput Mol Sci, 2025, 15
DOI: 10.1002/wcms.70003

How Robust Is the Ligand Binding Transition State?

Samik Bose, Samuel D. Lotz, Indrajit Deb, Megan Shuck, Kin Sing Stephen Lee and Alex Dickson

J. Am. Chem. Soc., 2023, 145, 25318
DOI: 10.1021/jacs.3c08940

Unravelling the interactions between small molecules and liposomal bilayers via molecular dynamics and thermodynamic modelling

Christopher M. Miles, Shane Cullen, Hussein Kenaan, Wenjie Gu, Gavin P. Andrews, Gabriele C. Sosso and Yiwei Tian

International Journal of Pharmaceutics, 2024, 660, 124367
DOI: 10.1016/j.ijpharm.2024.124367

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

Maria G. Khrenova

IJMS, 2025, 26, 7916
DOI: 10.3390/ijms26167916

Utilizing Molecular Dynamics Simulations, Machine Learning, Cryo-EM, and NMR Spectroscopy to Predict and Validate Protein Dynamics

Ahrum Son, Woojin Kim, Jongham Park, Wonseok Lee, Yerim Lee, Seongyun Choi and Hyunsoo Kim

IJMS, 2024, 25, 9725
DOI: 10.3390/ijms25179725

Application of Machine Learning Algorithms to Metadynamics for the Elucidation of the Binding Modes and Free Energy Landscape of Drug/Target Interactions: a Case Study

Gohar Ali Siddiqui, Julia A. Stebani, Darren Wragg, Phaedon‐Stelios Koutsourelakis, Angela Casini and Alessio Gagliardi

Chemistry A European J, 2023, 29
DOI: 10.1002/chem.202302375

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

Donghui Shao, Zhiteng Zhang, Xuyang Liu, Haohao Fu, Xueguang Shao and Wensheng Cai

J. Chem. Theory Comput., 2024, 20, 10393
DOI: 10.1021/acs.jctc.4c01282

Low-dimensional projection of reactivity classes in chemical reaction dynamics using supervised dimensionality reduction

Ryoichi Tanaka, Yuta Mizuno, Takuro Tsutsumi, Mikito Toda, Tetsuya Taketsugu and Tamiki Komatsuzaki

The Journal of Chemical Physics, 2024, 161
DOI: 10.1063/5.0230618

A Stochastic Landscape Approach for Protein Folding State Classification

Michael Faran, Dhiman Ray, Shubhadeep Nag, Umberto Raucci, Michele Parrinello and Gili Bisker

J. Chem. Theory Comput., 2024, 20, 5428
DOI: 10.1021/acs.jctc.4c00464

Analysis and sampling of molecular simulations with adversarial autoencoders

Guglielmo Tedeschi, Aleš Křenek and Vojtěch Spiwok

The Journal of Chemical Physics, 2026, 164
DOI: 10.1063/5.0320541

Prediction of Threonine-Tyrosine Kinase Receptor–Ligand Unbinding Kinetics with Multiscale Milestoning and Metadynamics

Lane W. Votapka, Anupam Anand Ojha, Naoya Asada and Rommie E. Amaro

J. Phys. Chem. Lett., 2024, 15, 10473
DOI: 10.1021/acs.jpclett.4c02332

All-around local structure classification with supervised learning: The example of crystal phases and dislocations in complex oxides

Jean Furstoss, Carlos R. Salazar, Philippe Carrez, Pierre Hirel and Julien Lam

Computer Physics Communications, 2025, 309, 109480
DOI: 10.1016/j.cpc.2024.109480

Assessing AF2’s ability to predict structural ensembles of proteins

Jakob R. Riccabona, Fabian C. Spoendlin, Anna-Lena M. Fischer, Johannes R. Loeffler, Patrick K. Quoika, Timothy P. Jenkins, James A. Ferguson, Eva Smorodina, Andreas H. Laustsen, Victor Greiff, Stefano Forli, Andrew B. Ward, Charlotte M. Deane and Monica L. Fernández-Quintero

Structure, 2024, 32, 2147
DOI: 10.1016/j.str.2024.09.001

Automated collective variable discovery for MFSD2A transporter from molecular dynamics simulations

Myongin Oh, Margarida Rosa, Hengyi Xie and George Khelashvili

Biophysical Journal, 2024, 123, 2934
DOI: 10.1016/j.bpj.2024.06.024

Protein representations: Encoding biological information for machine learning in biocatalysis

David Harding-Larsen, Jonathan Funk, Niklas Gesmar Madsen, Hani Gharabli, Carlos G. Acevedo-Rocha, Stanislav Mazurenko and Ditte Hededam Welner

Biotechnology Advances, 2024, 77, 108459
DOI: 10.1016/j.biotechadv.2024.108459

A Dynamic Evaluation of Cisplatin Encapsulation into [Pd2L4]4+ Metallacages in Solution

Julia A. Stebani, Iñigo Iribarren Aguirre, Darren Wragg, Alessio Gagliardi and Angela Casini

ACS Nano Med., 2026
DOI: 10.1021/acsnanomed.6c00024

Approximating Projections of Conformational Boltzmann Distributions with AlphaFold2 Predictions: Opportunities and Limitations

Benjamin P. Brown, Richard A. Stein, Jens Meiler and Hassane S. Mchaourab

J. Chem. Theory Comput., 2024, 20, 1434
DOI: 10.1021/acs.jctc.3c01081

Visualizing the Residue Interaction Landscape of Proteins by Temporal Network Embedding

Leon Franke and Christine Peter

J. Chem. Theory Comput., 2023, 19, 2985
DOI: 10.1021/acs.jctc.2c01228

Collective Variable-Based Enhanced Sampling: From Human Learning to Machine Learning

Haohao Fu, Hengwei Bian, Xueguang Shao and Wensheng Cai

J. Phys. Chem. Lett., 2024, 15, 1774
DOI: 10.1021/acs.jpclett.3c03542