Machine learning in the analysis of biomolecular simulations

Random Forests

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Deep learning

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Highly accurate protein structure prediction with AlphaFold

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‘GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers’

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The Elements of Statistical Learning

Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970 · see citing works

Reducing the dimensionality of data with neural networks

Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works

A logical calculus of the ideas immanent in nervous activity

McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566 · see citing works

Dynamic Programming

Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588 · see citing works

A Global Geometric Framework for Nonlinear Dimensionality Reduction

Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585 · see citing works

Principal component analysis: A review and recent developments

Jolliffe, I. T., & Cadima, J. (2016) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2015.0202 · Cited by 10,117 · see citing works

Essential dynamics of proteins

Amadei, A., Linssen, A. B. M., & Berendsen, H. J. C. (1993) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.340170408 · Cited by 3,487 · see citing works

Molecular Dynamics Simulation for All

Hollingsworth, S. A., & Dror, R. O. (2018) · Neuron · doi:10.1016/j.neuron.2018.08.011 · Cited by 3,313 · see citing works

Kernel methods in machine learning

Hofmann, T., Schölkopf, B., & Smola, A. J. (2008) · The Annals of Statistics · doi:10.1214/009053607000000677 · Cited by 1,758 · see citing works

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 · see citing works

Markov models of molecular kinetics: Generation and validation

Prinz, J.-H., Wu, H., Sarich, M., Keller, B., Senne, M., Held, M., Chodera, J. D., Schütte, C., & Noé, F. (2011) · The Journal of Chemical Physics · doi:10.1063/1.3565032 · Cited by 1,275 · see citing works

A Multiscale Coarse-Graining Method for Biomolecular Systems

Izvekov, S., & Voth, G. A. (2005) · The Journal of Physical Chemistry B · doi:10.1021/jp044629q · Cited by 1,192 · see citing works

Biomolecular simulation: a computational microscope for molecular biology.

Dror, R., Dirks, R. M., Grossman, J. P., Xu, H., & Shaw, D. (2012) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-042910-155245 · Cited by 1,176 · see citing works

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 1,135 · see citing works

Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection

Campello, R. J. G. B., Moulavi, D., Zimek, A., & Sander, J. (2015) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2733381 · Cited by 915 · see citing works

Separation of a mixture of independent signals using time delayed correlations

Molgedey, L., & Schuster, H. G. (1994) · Physical Review Letters · doi:10.1103/physrevlett.72.3634 · Cited by 882 · see citing works

Anton, a special-purpose machine for molecular dynamics simulation

Shaw, D., Deneroff, M. M., Dror, R., Kuskin, J., Larson, R. H., Salmon, J., Young, C., Batson, B., Bowers, K., Chao, J. C., et al. (2008) · CACM · doi:10.1145/1364782.1364802 · Cited by 588 · see citing works

Mathematics for Machine Learning

Deisenroth, M. P., Faisal, A. A., & Ong, C. S. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108679930 · Cited by 583 · see citing works

Anton 2: Raising the Bar for Performance and Programmability in a Special-Purpose Molecular Dynamics Supercomputer

Shaw, D. E., Grossman, J. P., Bank, J. A., Batson, B., Butts, J. A., Chao, J. C., Deneroff, M. M., Dror, R., Even, A., Fenton, C. H., et al. (2014) · International Conference for High Performance Computing, Networking, Storage and Analysis · doi:10.1109/sc.2014.9 · Cited by 567 · see citing works

Screen Savers of the World Unite!

Shirts, M., & Pande, V. (2000) · Science · doi:10.1126/science.290.5498.1903 · Cited by 550 · see citing works

Machine Learning of Coarse-Grained Molecular Dynamics Force Fields

Wang, J., Olsson, S., Wehmeyer, C., Pérez, A., Charron, N. E., Fabritiis, G. D., Noé, F., & Clementi, C. (2019) · ACS Central Science · doi:10.1021/acscentsci.8b00913 · Cited by 519 · see citing works

Energy landscape of a small peptide revealed by dihedral angle principal component analysis

Mu, Y., Nguyen, P., & Stock, G. (2004) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.20310 · Cited by 425 · see citing works

To milliseconds and beyond: challenges in the simulation of protein folding

Lane, T. J., Shukla, D., Beauchamp, K. A., & Pande, V. S. (2013) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2012.11.002 · Cited by 409 · see citing works

Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics

Wehmeyer, C., & Noé, F. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5011399 · Cited by 401 · see citing works

Enhanced sampling in molecular dynamics.

Yang, Y., Shao, Q., Zhang, J., Yang, L.-J., & Gao, Y. (2019) · Journal of Chemical Physics · doi:10.1063/1.5109531 · Cited by 362 · see citing works

Reweighted autoencoded variational Bayes for enhanced sampling (RAVE)

Ribeiro, J. M. L., Collado, P. B., Wang, Y., & Tiwary, P. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5025487 · Cited by 332 · see citing works

Low-dimensional, free-energy landscapes of protein-folding reactions by nonlinear dimensionality reduction

Das, P., Moll, M., Stamati, H., Kavraki, L. E., & Clementi, C. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0603553103 · Cited by 331 · see citing works

Dihedral angle principal component analysis of molecular dynamics simulations.

Altis, A., Nguyen, P., Hegger, R., & Stock, G. (2007) · Journal of Chemical Physics · doi:10.1063/1.2746330 · Cited by 327 · see citing works

An Efficient Method for Sampling the Essential Subspace of Proteins

Amadei, A., Linssen, A. B. M., de Groot, B. L., van Aalten, D. M. F., & Berendsen, H. J. C. (1996) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.1996.10508874 · Cited by 315 · see citing works

Metadynamics with Adaptive Gaussians

Branduardi, D., Bussi, G., & Parrinello, M. (2012) · Journal of Chemical Theory and Computation · doi:10.1021/ct3002464 · Cited by 306 · see citing works

Determination of reaction coordinates via locally scaled diffusion map

Rohrdanz, M. A., Zheng, W., Maggioni, M., & Clementi, C. (2011) · The Journal of Chemical Physics · doi:10.1063/1.3569857 · Cited by 292 · see citing works

Multiscale Simulations of Biological Membranes: The Challenge To Understand Biological Phenomena in a Living Substance

Enkavi, G., Javanainen, M., Kulig, W., Róg, T., & Vattulainen, I. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00538 · Cited by 285 · see citing works

Principal component analysis of molecular dynamics: On the use of Cartesian vs. internal coordinates

Sittel, F., Jain, A., & Stock, G. (2014) · The Journal of Chemical Physics · doi:10.1063/1.4885338 · Cited by 268 · see citing works

Molecular enhanced sampling with autoencoders: On‐the‐fly collective variable discovery and accelerated free energy landscape exploration

Chen, W., & Ferguson, A. L. (2018) · Journal of Computational Chemistry · doi:10.1002/jcc.25520 · Cited by 234 · see citing works

Atomic-Level Characterization of the Ensemble of the Aβ(1–42) Monomer in Water Using Unbiased Molecular Dynamics Simulations and Spectral Algorithms

Sgourakis, N. G., Merced-Serrano, M., Boutsidis, C., Petros Drineas, B., Du, Z., Wang, C., & Garcı́a, A. E. (2010) · Journal of Molecular Biology · doi:10.1016/j.jmb.2010.10.015 · Cited by 216 · see citing works

Kinetic Distance and Kinetic Maps from Molecular Dynamics Simulation

Noé, F., & Clementi, C. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00553 · Cited by 214 · see citing works

Statistical clustering techniques for the analysis of long molecular dynamics trajectories: analysis of 2.2-ns trajectories of YPGDV.

Karpen, M. E., Tobias, D., & Brooks, C. (1993) · Biochemistry · doi:10.1021/bi00053a005 · Cited by 211 · see citing works

Prediction of Protein–Protein Interaction Sites in Sequences and 3D Structures by Random Forests

Šikić, M., Tomić, S., & Vlahoviček, K. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000278 · Cited by 173 · see citing works

tICA-Metadynamics: Accelerating Metadynamics by Using Kinetically Selected Collective Variables

Sultan, M. M., & Pande, V. S. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00182 · Cited by 167 · see citing works

Systematic determination of order parameters for chain dynamics using diffusion maps

Ferguson, A. L., Panagiotopoulos, A. Z., Debenedetti, P. G., & Kevrekidis, I. G. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1003293107 · Cited by 165 · see citing works

Modeling Molecular Kinetics with tICA and the Kernel Trick

Schwantes, C. R., & Pande, V. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/ct5007357 · Cited by 157 · see citing works

Detection of Functional Modes in Protein Dynamics

Hub, J. S., & de Groot, B. L. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000480 · Cited by 150 · see citing works

The diversity in the applications of partial least squares: an overview

Mehmood, T., & Ahmed, B. (2015) · Journal of Chemometrics · doi:10.1002/cem.2762 · Cited by 139 · see citing works

Essential dynamics: foundation and applications

Daidone, I., & Amadei, A. (2012) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1099 · Cited by 138 · see citing works

Principal component and clustering analysis on molecular dynamics data of the ribosomal L11·23S subdomain

Wolf, A., & Kirschner, K. N. (2012) · Journal of Molecular Modeling · doi:10.1007/s00894-012-1563-4 · Cited by 132 · see citing works

Chapter 13 Principal Components Analysis: A Review of its Application on Molecular Dynamics Data

Stein, S. A. M., Loccisano, A. E., Firestine, S. M., & Evanseck, J. D. (2006) · Annual reports in computational chemistry · doi:10.1016/s1574-1400(06)02013-5 · Cited by 132 · see citing works

Coupling Molecular Dynamics and Deep Learning to Mine Protein Conformational Space

Degiacomi, M. T. (2018) · Structure · doi:10.1016/j.str.2019.03.018 · Cited by 127 · see citing works

Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials

John, S. T., & Cśanyi, G. (2017) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.7b09636 · Cited by 124 · see citing works

Identifying ligand binding sites and poses using GPU-accelerated Hamiltonian replica exchange molecular dynamics

Wang, K., Chodera, J., Yang, Y., & Shirts, M. (2013) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-013-9689-8 · Cited by 115 · see citing works

Algorithms for clustering molecular dynamics configurations

Torda, A. E., & van Gunsteren, W. F. (1994) · Journal of Computational Chemistry · doi:10.1002/jcc.540151203 · Cited by 106 · see citing works

Molecular Dynamics Fingerprints (MDFP): Machine Learning from MD Data To Predict Free-Energy Differences

Riniker, S. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00778 · Cited by 106 · see citing works

Metadynamics in Essential Coordinates: Free Energy Simulation of Conformational Changes

Spiwok, V., Lipovová, P., & Králová, B. (2007) · The Journal of Physical Chemistry B · doi:10.1021/jp068587c · Cited by 103 · see citing works

Can Principal Components Yield a Dimension Reduced Description of Protein Dynamics on Long Time Scales?

Lange, O. F., & Grubmüller, H. (2006) · The Journal of Physical Chemistry B · doi:10.1021/jp062548j · Cited by 100 · see citing works

Protein conformational landscapes: Energy minimization and clustering of a long molecular dynamics trajectory

Troyer, J. M., & Cohen, F. E. (1995) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.340230111 · Cited by 96 · see citing works

Effective Force Field for Liquid Hydrogen Fluoride from Ab Initio Molecular Dynamics Simulation Using the Force-Matching Method

Izvekov, S., & Voth, G. A. (2005) · The Journal of Physical Chemistry B · doi:10.1021/jp0456685 · Cited by 77 · see citing works

Hierarchical Time-Lagged Independent Component Analysis: Computing Slow Modes and Reaction Coordinates for Large Molecular Systems

Pérez‐Hernández, G., & Noé, F. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00738 · Cited by 76 · see citing works

Ubiquitin Dynamics in Complexes Reveal Molecular Recognition Mechanisms Beyond Induced Fit and Conformational Selection

Peters, J. H., & de Groot, B. L. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002704 · Cited by 75 · see citing works

Flooding in GROMACS: Accelerated barrier crossings in molecular dynamics

Lange, O. F., Schäfer, L. V., & Grubmüller, H. (2006) · Journal of Computational Chemistry · doi:10.1002/jcc.20473 · Cited by 74 · see citing works

Coarse-grained model for phospholipid/cholesterol bilayer employing inverse Monte Carlo with thermodynamic constraints

Murtola, T. J., Falck, E., Karttunen, M., & Vattulainen, I. (2007) · The Journal of Chemical Physics · doi:10.1063/1.2646614 · Cited by 72 · see citing works

Partial Least-Squares Functional Mode Analysis: Application to the Membrane Proteins AQP1, Aqy1, and CLC-ec1

Krivobokova, T., Briones, R., Hub, J. S., Munk, A., & de Groot, B. L. (2012) · Biophysical Journal · doi:10.1016/j.bpj.2012.07.022 · Cited by 71 · see citing works

Metadynamics in the conformational space nonlinearly dimensionally reduced by Isomap

Spiwok, V., & Králová, B. (2011) · The Journal of Chemical Physics · doi:10.1063/1.3660208 · Cited by 70 · see citing works

Gaussian-Mixture Umbrella Sampling

Maragakis, P., van der Vaart, A., & Karplus, M. (2009) · The Journal of Physical Chemistry B · doi:10.1021/jp808381s · Cited by 69 · see citing works

A Machine Learning Approach for the Discovery of Ligand-Specific Functional Mechanisms of GPCRs

Plante, A., Shore, D. M., Morra, G., Khelashvili, G., & Weinstein, H. (2019) · Molecules · doi:10.3390/molecules24112097 · Cited by 68 · see citing works

Toward Achieving Efficient and Accurate Ligand-Protein Unbinding with Deep Learning and Molecular Dynamics through RAVE.

Ribeiro, J. M. L., & Tiwary, P. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00869 · Cited by 65 · see citing works

Principal component analysis on a torus: Theory and application to protein dynamics.

Sittel, F., Filk, T., & Stock, G. (2017) · Journal of Chemical Physics · doi:10.1063/1.4998259 · Cited by 64 · see citing works

Modeling and enhanced sampling of molecular systems with smooth and nonlinear data-driven collective variables

Hashemian, B., Millán, D., & Arroyo, M. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4830403 · Cited by 63 · see citing works

Kernel-Based Machine Learning for Efficient Simulations of Molecular Liquids

Scherer, C., Scheid, R., Andrienko, D., & Bereau, T. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b01256 · Cited by 60 · see citing works

Systematic characterization of protein folding pathways using diffusion maps: Application to Trp-cage miniprotein

Kim, S. B., Dsilva, C. J., Kevrekidis, I. G., & Debenedetti, P. G. (2015) · The Journal of Chemical Physics · doi:10.1063/1.4913322 · Cited by 58 · see citing works

H95 Is a pH-Dependent Gate in Aquaporin 4

Kaptan, S., Assentoft, M., Schneider, H. P., Fenton, R. A., Deitmer, J. W., MacAulay, N., & de Groot, B. L. (2015) · Structure · doi:10.1016/j.str.2015.08.020 · Cited by 58 · see citing works

Fuzzy cluster analysis of molecular dynamics trajectories

Gordon, H. L., & Somorjai, R. L. (1992) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.340140211 · Cited by 57 · see citing works

Light Harvesting by Lamellar Chromatophores in Rhodospirillum photometricum

Chandler, D. E., Strümpfer, J., Şener, M., Simon Scheuring, A., & Schulten, K. (2014) · Biophysical Journal · doi:10.1016/j.bpj.2014.04.030 · Cited by 56 · see citing works

Interpretable embeddings from molecular simulations using Gaussian mixture variational autoencoders

Varolgüneş, Y. B., Bereau, T., & Rudzinski, J. F. (2019) · Machine Learning: Science and Technology · doi:10.1088/2632-2153/ab80b7 · Cited by 50 · see citing works

Toward Identification of the Reaction Coordinate Directly from the Transition State Ensemble Using the Kernel PCA Method

Antoniou, D., & Schwartz, S. D. (2011) · The Journal of Physical Chemistry B · doi:10.1021/jp111682x · Cited by 46 · see citing works

InfleCS: Clustering Free Energy Landscapes with Gaussian Mixtures

Westerlund, A. M., & Delemotte, L. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00454 · Cited by 43 · see citing works

Cluster analysis of molecular simulation trajectories for systems where both conformation and orientation of the sampled states are important

Abramyan, T. M., Snyder, J. A., Thyparambil, A. A., Stuart, S. J., & Latour, R. A. (2016) · Journal of Computational Chemistry · doi:10.1002/jcc.24416 · Cited by 42 · see citing works

Gaussian Mixture Based Enhanced Sampling For Statics And Dynamics.

Debnath, J., & Parrinello, M. (2019) · Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c01125 · Cited by 41 · see citing works

A machine learning approach to estimation of phase diagrams for three-component lipid mixtures

Aghaaminiha, M., Ghanadian, S. A., Ahmadi, E., & Farnoud, A. M. (2020) · Biochimica et Biophysica Acta (BBA) - Biomembranes · doi:10.1016/j.bbamem.2020.183350 · Cited by 30 · see citing works

Visualizing correlated motion with HDBSCAN clustering

Melvin, R. L., Xiao, J., Godwin, R. C., Berenhaut, K. S., & Salsbury, F. R. (2017) · Protein Science · doi:10.1002/pro.3268 · Cited by 26 · see citing works

Automatic Selection of Order Parameters in the Analysis of Large Scale Molecular Dynamics Simulations

Sultan, M. M., Kiss, G., Shukla, D., & Pande, V. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct500353m · Cited by 23 · see citing works

Machine Learning Classification Model for Functional Binding Modes of TEM-1 β-Lactamase

Wang, F., Shen, L., Zhou, H.-Y., Wang, S., Wang, X., & Tao, P. (2019) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2019.00047 · Cited by 22 · see citing works

DNA Binding Dynamics and Energetics of Cobalt, Nickel, and Copper Metallopeptides

Galindo‐Murillo, R., & Cheatham, T. E. (2014) · ChemMedChem · doi:10.1002/cmdc.201402020 · Cited by 18 · see citing works

Spotting the difference in molecular dynamics simulations of biomolecules

Sakuraba, S., & Kono, H. (2016) · The Journal of Chemical Physics · doi:10.1063/1.4961227 · Cited by 18 · see citing works

Overcoming the Heuristic Nature of k ‑Means Clustering: Identification and Characterization of Binding Modes from Simulations of Molecular Recognition Complexes

Bremer, P. L., Boer, D. D., Alvarado, W., Martinez, X., & Sorin, E. J. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b01137 · Cited by 18 · see citing works

Role of Pore-Lining Residues in Defining the Rate of Water Conduction by Aquaporin-0

Saboe, P. O., Rapisarda, C., Kaptan, S., Hsiao, Y., Summers, S. R., Zorzi, R. D., Dukovski, D., Yu, J., de Groot, B. L., Kumar, M., et al. (2017) · Biophysical Journal · doi:10.1016/j.bpj.2017.01.026 · Cited by 16 · see citing works

Millisecond-Long Molecular Dynamics Simulations of Proteins on a Special-Purpose Machine

Shaw, D. E. (2013) · Biophysical Journal · doi:10.1016/j.bpj.2012.11.289 · Cited by 14 · see citing works

AlphaFold2 transmembrane protein structure prediction shinesPreprint

Hegedűs, T., Geisler, M., Lukács, G., & Farkas, B. (2021) · bioRxiv · doi:10.1101/2021.08.21.457196 · Cited by 8 · see citing works

Density-Based Clustering of Small Peptide Conformations Sampled from a Molecular Dynamics Simulation

Kim, M., Choi, S., Kim, J., Choi, K., Shin, J., Kang, S., Choi, Y.-J., & Jung, D. H. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci800434e · Cited by 7 · see citing works

Use of TSAR as a new tool to analyze the molecular dynamics trajectories of proteins

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Exploring the development and applications of sustainable natural fiber composites: A review from a nanoscale perspective

Feng, Y., Hao, H., Lu, H., Chow, C. L., & Lau, D. (2024) · Composites Part B Engineering · doi:10.1016/j.compositesb.2024.111369 · Cited by 221 · see citing works

AlphaFold, Artificial Intelligence (AI), and Allostery

Nussinov, R., Zhang, M., Liu, Y., & Jang, H. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c04346 · Cited by 190 · see citing works

Molecular dynamics simulation of an entire cell

Stevens, J. A., Grünewald, F., van Tilburg, P. A. M., König, M., Gilbert, B. R., Brier, T. A., Thornburg, Z. R., Luthey-Schulten, Z., & Marrink, S. J. (2023) · Frontiers in Chemistry · doi:10.3389/fchem.2023.1106495 · Cited by 156 · see citing works

AutoElbow: An Automatic Elbow Detection Method for Estimating the Number of Clusters in a Dataset

Onumanyi, A. J., Molokomme, D. N., Isaac, S. J., & Abu-Mahfouz, A. M. I. (2022) · Applied Sciences · doi:10.3390/app12157515 · Cited by 82 · see citing works

Machine learning prediction of deep eutectic solvents pretreatment of lignocellulosic biomass

Xu, H., Dong, C., Wang, W., Liu, Y., Li, B., & Liu, F. (2023) · Industrial Crops and Products · doi:10.1016/j.indcrop.2023.116431 · Cited by 60 · see citing works

Open-Source Machine Learning in Computational Chemistry

Hagg, A., & Kirschner, K. N. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00643 · Cited by 62 · see citing works

Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence

Son, A., Park, J., Kim, W., Yoon, Y., Lee, S., Park, Y., & Kim, H. (2024) · Molecules · doi:10.3390/molecules29194626 · Cited by 42 · see citing works

Molecular dynamics simulations for the structure-based drug design: targeting small-GTPases proteins

Parise, A., Cresca, S., & Magistrato, A. (2024) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2024.2387856 · Cited by 27 · see citing works

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

Roccatano, D. (2025) · WIREs Computational Molecular Science · doi:10.1002/wcms.70060 · Cited by 22 · see citing works

Molecular Simulation Meets Machine Learning

Sadus, R. J. (2023) · Journal of Chemical & Engineering Data · doi:10.1021/acs.jced.3c00553 · Cited by 15 · see citing works

No dance, no partner! A tale of receptor flexibility in docking and virtual screening

Basciu, A., Callea, L., Motta, S., Bonvin, A. M. J. J., Bonati, L., & Vargiu, A. V. (2022) · Annual reports in medicinal chemistry · doi:10.1016/bs.armc.2022.08.006 · Cited by 14 · see citing works

tICA-Metadynamics for Identifying Slow Dynamics in Membrane Permeation

Oh, M., da Hora, G. C. A., & Swanson, J. M. J. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00526 · Cited by 7 · see citing works

Physically interpretable performance metrics for clustering

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Recent Advance in Electrochemical Chiral Recognition Based on Biomaterials (2019–2024)

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Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation

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