Machine learning heralding a new development phase in molecular dynamics simulations

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(4)

Published: March 29, 2024

Abstract Molecular dynamics (MD) simulations are a key computational chemistry technique that provide dynamic insight into the underlying atomic-level processes in system under study. These insights not only improve our understanding of molecular world, but also aid design experiments and targeted interventions. Currently, MD is associated with several limitations, most important which are: insufficient sampling, inadequate accuracy atomistic models, challenges proper analysis interpretation obtained trajectories. Although numerous efforts have been made to address these more effective solutions still needed. The recent development artificial intelligence, particularly machine learning (ML), offers exciting opportunities MD. In this review we aim familiarize readers basics while highlighting its limitations. main focus on exploring integration deep simulations. advancements by ML systematically outlined, including ML-based force fields, techniques for improved conformational space innovative methods trajectory analysis. Additionally, implications intelligence discussed. While potential ML-MD fusion clearly established, further applications needed confirm superiority over traditional methods. This comprehensive overview new perspectives MD, has opened up, serves as gentle introduction phase development.

Language: Английский

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

Journal of Chemical Theory and Computation, Journal Year: 2024, Volume and Issue: 20(3), P. 1434 - 1447

Published: Jan. 12, 2024

Protein thermodynamics is intimately tied to biological function and can enable processes such as signal transduction, enzyme catalysis, molecular recognition. The relative free energies of conformations that contribute these functional equilibria evolved for the physiology organism. Despite importance understanding developing treatments disease, computational experimental methods capable quantifying energetic determinants are limited systems modest size. Recently, it has been demonstrated artificial intelligence system AlphaFold2 be manipulated produce structurally valid protein conformational ensembles. Here, we extend studies explore extent which contact distance distributions approximate projections Boltzmann distributions. For this purpose, examine joint probability inter-residue distances along functionally relevant collective variables several systems. Our suggest normalized correlate with conformation probabilities obtained other but they suffer from peak broadening. We also find sensitive point mutations. Overall, anticipate our findings will valuable community seeks model changes in large biomolecular

Language: Английский

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Computational Methods for Modeling Lipid-Mediated Active Pharmaceutical Ingredient Delivery

Molecular Pharmaceutics, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 29, 2025

Lipid-mediated delivery of active pharmaceutical ingredients (API) opened new possibilities in advanced therapies. By encapsulating an API into a lipid nanocarrier (LNC), one can safely deliver APIs not soluble water, those with otherwise strong adverse effects, or very fragile ones such as nucleic acids. However, for the rational design LNCs, detailed understanding composition-structure-function relationships is missing. This review presents currently available computational methods LNC investigation, screening, and design. The state-of-the-art physics-based approaches are described, focus on molecular dynamics simulations all-atom coarse-grained resolution. Their strengths weaknesses discussed, highlighting aspects necessary obtaining reliable results simulations. Furthermore, machine learning, i.e., data-based approach to lipid-mediated introduced. data produced by experimental theoretical provide valuable insights. Processing these help optimize LNCs better performance. In final section this Review, computer reviewed, specifically addressing compatibility

Language: Английский

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

ACS Catalysis, Journal Year: 2023, Volume and Issue: 13(17), P. 11455 - 11493

Published: Aug. 15, 2023

Within this Perspective, we critically reflect on the role of first-principles molecular dynamics (MD) simulations in unraveling catalytic function within zeolites under operating conditions. First-principles MD refer to methods where nuclei is followed time by integrating Newtonian equations motion a potential energy surface that determined solving quantum-mechanical many-body problem for electrons. Catalytic solids used industrial applications show an intriguing high degree complexity, with phenomena taking place at broad range length and scales. Additionally, state catalyst depend conditions, such as temperature, moisture, presence water, etc. Herein means series exemplary cases how are instrumental unravel complexity scale. Examples nature reactive species higher temperatures may drastically change compared lower active sites dynamically upon exposure water. To simulate rare events, need be combination enhanced sampling techniques efficiently sample low-probability regions phase space. Using these techniques, it shown competitive pathways conditions can discovered transition explored. Interestingly, also study hindered diffusion The clearly illustrate reveal insights into which could not using static or local approaches only few points considered (PES). Despite advantages, some major hurdles still exist fully integrate standard computational workflow use output input multiple length/time scale aim bridge reactor First all, needed allow us evaluate interatomic forces accuracy, albeit much cost currently density functional theory (DFT) methods. DFT limits attainable scales hundreds picoseconds nanometers, smaller than realistic particle dimensions encountered catalysis process. One solution construct machine learning potentials (MLPs), numerical derived from underlying data, subsequent simulations. As such, longer reached; however, quite research necessary MLPs complex systems industrially catalysts. Second, most make collective variables (CVs), mostly based chemical intuition. explore networks simulations, automatic discovery CVs do rely priori definition CVs. Recently, various data-driven have been proposed, explored systems. Lastly, investigate events. We hope rise more efficient describe PES, will future able processes catalysis. This might lead consistent dynamic description all steps─diffusion, adsorption, reaction─as they take level.

Language: Английский

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From byte to bench to bedside: molecular dynamics simulations and drug discovery

BMC Biology, Journal Year: 2023, Volume and Issue: 21(1)

Published: Dec. 29, 2023

Molecular dynamics (MD) simulations and computer-aided drug design (CADD) have advanced substantially over the past two decades, thanks to continuous computer hardware software improvements. Given these advancements, MD are poised become even more powerful tools for investigating dynamic interactions between potential small-molecule drugs their target proteins, with significant implications pharmacological research.

Language: Английский

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

The Journal of Physical Chemistry Letters, Journal Year: 2024, Volume and Issue: 15(6), P. 1774 - 1783

Published: Feb. 8, 2024

Enhanced-sampling algorithms relying on collective variables (CVs) are extensively employed to study complex (bio)chemical processes that not amenable brute-force molecular simulations. The selection of appropriate CVs characterizing the slow movement modes is paramount importance for reliable and efficient enhanced-sampling In this Perspective, we first review application limitations obtained from chemical geometrical intuition. We also introduce path-sampling algorithms, which can identify path-like in a high-dimensional free-energy space. Machine-learning offer viable approach finding suitable by analyzing trajectories preliminary discuss both performance machine-learning-derived simulations experimental models challenges involved applying these realistic, assemblies. Moreover, provide prospective view potential advancements machine-learning development field

Language: Английский

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Overcoming the timescale barrier in molecular dynamics: Transfer operators, variational principles and machine learning

Acta Numerica, Journal Year: 2023, Volume and Issue: 32, P. 517 - 673

Published: May 1, 2023

One of the main challenges in molecular dynamics is overcoming ‘timescale barrier’: many realistic systems, biologically important rare transitions occur on timescales that are not accessible to direct numerical simulation, even largest or specifically dedicated supercomputers. This article discusses how circumvent timescale barrier by a collection transfer operator-based techniques have emerged from dynamical systems theory, mathematics and machine learning over last two decades. We will focus operators can be used approximate behaviour long timescales, review introduction this approach into dynamics, outline respective as well algorithmic development, early numerics-based methods, via variational reformulations, modern data-based utilizing improving concepts learning. Furthermore, its relation event simulation explained, revealing broad equivalence principles for long-time quantities dynamics. The mainly take mathematical perspective leave application real-world more than 1000 research articles already written subject.

Language: Английский

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Lignin-based porous carbon adsorbents for CO₂ capture

Chemical Society Reviews, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

A major driver of global climate change is the rising concentration atmospheric CO

Language: Английский

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All-atom Molecular Dynamics Simulations of Polymer and Polyelectrolyte Brushes

Chemical Communications, Journal Year: 2024, Volume and Issue: 60(48), P. 6093 - 6129

Published: Jan. 1, 2024

New discoveries on polymer and polyelectrolyte brush systems the corresponding brush-supported ions water, arising from employing all-atom molecular dynamics simulations, have been thoroughly reviewed.

Language: Английский

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A Stochastic Landscape Approach for Protein Folding State Classification

Journal of Chemical Theory and Computation, Journal Year: 2024, Volume and Issue: 20(13), P. 5428 - 5438

Published: June 26, 2024

Protein folding is a critical process that determines the functional state of proteins. Proper essential for proteins to acquire their three-dimensional structures and execute biological role, whereas misfolded can lead various diseases, including neurodegenerative disorders like Alzheimer's Parkinson's. Therefore, deeper understanding protein vital disease mechanisms developing therapeutic strategies. This study introduces Stochastic Landscape Classification (SLC), an innovative, automated, nonlearning algorithm quantitatively analyzes dynamics. Focusing on collective variables (CVs) – low-dimensional representations complex dynamical systems molecular dynamics (MD) macromolecules SLC approach segments CVs into distinct macrostates, revealing pathway explored by MD simulations. The segmentation achieved analyzing changes in CV trends clustering these using standard density-based spatial applications with noise (DBSCAN) scheme. Applied MD-based trajectories Chignolin Trp-Cage proteins, demonstrates apposite accuracy, validated comparing classification metrics against ground-truth data. These affirm efficacy capturing intricate offer method evaluate select most informative CVs. practical application this technique lies its ability provide detailed, quantitative description processes, significant implications manipulating behavior industrial pharmaceutical contexts.

Language: Английский

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