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This is a copy of a conversation between ChatGPT & Anonymous.这是 ChatGPT 与匿名者之间对话的副本。

Report content报告内容

You said:

I am a Ph.D. candidate in chemistry, considering molecular dynamics as my research topic. I already have some understanding of the subject. Could you summarize the latest academic advancements in the field of molecular dynamics, including technology and applications? If possible, please provide references.我是一个化学专业的博士生，考虑将分子动力学作为我的研究课题。我已经对这个主题有一些了解。你能总结一下分子动力学领域的最新学术进展，包括技术和应用吗？如果可能的话，请提供参考文献。

ChatGPT said:

I’d be happy to help with that. To provide a thorough summary of the latest academic advancements in molecular dynamics, could you specify:我很乐意帮忙。为了提供分子动力学最新学术进展的全面总结，您能否具体说明：

A preferred time frame for the latest research (e.g., past 2 years, past 5 years)?一个最新研究（例如，过去 2 年，过去 5 年）的优先时间范围？

Specific subfields or applications of molecular dynamics you are most interested in (e.g., drug discovery, materials science, biophysics, enzymatic reactions, etc.)?您最感兴趣的分子动力学特定子领域或应用（例如，药物发现、材料科学、生物物理学、酶促反应等）？

If you are looking for advancements in algorithms, hardware acceleration (e.g., GPU-based MD, quantum computing), or software developments?如果您正在寻找算法的进步、硬件加速（例如基于 GPU 的 MD、量子计算）或软件开发？

Would you like references from high-impact journals or a mix of sources including preprints and conference papers?您希望引用来自高影响力期刊的参考文献，还是包括预印本和会议论文在内的多种来源？

Let me know so I can tailor my research to your needs!让我知道，这样我可以根据您的需求定制我的研究！

You said:

I am looking for research from the past five years.

My primary focus is on molecular dynamics in drug discovery and materials science.

I am not particularly interested in algorithmic advancements, but you may briefly mention them.

Please prioritize high-impact journals.我在寻找过去五年的研究。

我的主要关注领域是药物发现和材料科学中的分子动力学。

我对算法进步不太感兴趣，但你可以简要提及。

请优先考虑高影响力期刊。

ChatGPT said:

Great! I will research the latest advancements in molecular dynamics within the past five years, specifically in drug discovery and materials science. I'll prioritize findings from high-impact journals while also briefly mentioning relevant algorithmic advancements. I'll get back to you with a summary and references soon.太好了！我将研究过去五年内分子动力学领域的最新进展，特别是在药物发现和材料科学方面。我会优先考虑来自高影响力期刊的研究成果，同时简要提及相关的算法进展。我很快会给你发送总结和参考文献。

You said:

Please start researching请开始研究

ChatGPT said:

Great! I will begin researching the latest advancements in molecular dynamics from the past five years, focusing on drug discovery and materials science. I will prioritize findings from high-impact journals and briefly mention relevant algorithmic advancements.太好了！我将开始研究过去五年分子动力学领域的最新进展，重点关注药物发现和材料科学。我将优先考虑来自高影响力期刊的研究成果，并简要提及相关的算法进展。

I'll get back to you with a summary and references soon.我很快会给你发送一份总结和参考文献。

You said:

You suppose to invoke the deep research function你应当调用深度研究功能

ChatGPT said:

Great! I will begin researching the latest advancements in molecular dynamics from the past five years, focusing on drug discovery and materials science. I will prioritize findings from high-impact journals and briefly mention relevant algorithmic advancements. I will compile key breakthroughs, emerging techniques, and their practical implications with references to academic papers.太好了！我将开始研究过去五年分子动力学领域的最新进展，重点关注药物发现和材料科学。我将优先考虑来自高影响力期刊的研究成果，并简要提及相关的算法进步。我将整理关键突破、新兴技术和它们的实际应用，并引用学术论文。

I'll update you as soon as I have the findings.我将一有发现就立即通知您。

Recent Advancements in Molecular Dynamics for Drug Discovery and Materials Science (2019–2024)

Molecular dynamics (MD) simulations have seen significant advancements in the past five years, extending their impact on both drug discovery and materials science. Improved algorithms and computing power now enable more accurate and longer simulations, helping researchers capture complex biomolecular interactions and material behaviors that were previously inaccessible. Below is a structured overview of key breakthroughs, emerging techniques, and practical implications in these fields, with references to representative high-impact studies.分子动力学（MD）模拟在过去五年中取得了显著进展，扩大了其在药物发现和材料科学领域的影响。改进的算法和计算能力现在使得模拟更加精确和持久，帮助研究人员捕捉到以前无法接触到的复杂生物分子相互作用和材料行为。以下是这些领域关键突破、新兴技术和实际应用的概述，并引用了代表性高影响力研究。

Molecular Dynamics in Drug Discovery分子动力学在药物发现中的应用

MD simulations have become integral to modern drug discovery, providing atomic-level insight into how drugs bind and how proteins change shape. Recent studies in top-tier journals highlight MD’s role in identifying new druggable sites, understanding binding mechanisms, and guiding drug design decisions:分子动力学模拟已成为现代药物发现的重要组成部分，提供了对药物如何结合以及蛋白质如何改变形状的原子级洞察。顶级期刊的最新研究突出了分子动力学在识别新的药物靶点、理解结合机制和指导药物设计决策中的作用：

Revealing Cryptic and Allosteric Binding Sites: MD has proven invaluable for uncovering cryptic pockets – transient binding sites not seen in static crystal structures. Enhanced-sampling simulations (e.g. using mixed solvents, metadynamics, or Markov state models) can capture proteins briefly opening hidden pockets​
pmc.ncbi.nlm.nih.gov
. For example, simulations of a fatty-acid GPCR (FFAR1) revealed a previously unknown pocket that closes when the native ligand leaves; a compound designed to bind this pocket was shown to act as an allosteric agonist​
pnas.org
. Such findings illustrate how MD can expose “hidden” allosteric sites and enable novel drug design strategies.揭示隐秘和变构结合位点：分子动力学（MD）已被证明在揭示隐秘口袋——在静态晶体结构中看不到的瞬时结合位点——方面极为有价值。增强采样模拟（例如使用混合溶剂、元动力学或马尔可夫状态模型）可以捕捉蛋白质短暂打开隐藏口袋。例如，对脂肪酸 G 蛋白偶联受体（FFAR1）的模拟揭示了一个以前未知的口袋，当天然配体离开时该口袋关闭；一种旨在结合此口袋的化合物被证明是一种变构激动剂。这样的发现说明了分子动力学如何揭示“隐藏”的变构位点，并使新的药物设计策略成为可能。

Mechanistic Insights into Target Dynamics: Long-timescale MD and accelerated methods now shed light on how drug targets change conformation and respond to ligands. G protein-coupled receptors (GPCRs), which are notoriously dynamic drug targets, have been a major focus. Enhanced MD techniques (e.g. Gaussian accelerated MD) were used to simulate the full activation cycle of a GPCR (adenosine A1 receptor) within hundreds of nanoseconds​
pnas.org
. The simulations revealed intermediate states of receptor activation and G-protein coupling that complement experimental structures​
pnas.org
​
pnas.org
. These insights help explain how different ligands (agonists, partial agonists, antagonists) stabilize certain receptor states, which is crucial for designing drugs with desired efficacy and signaling profiles.机制见解：针对靶动态：长时程分子动力学和加速方法现在揭示了药物靶点如何改变构象并响应配体。G 蛋白偶联受体（GPCRs），作为臭名昭著的动态药物靶点，一直是主要的研究对象。增强的分子动力学技术（例如高斯加速分子动力学）被用于在数百纳秒内模拟 GPCR（腺苷 A1 受体）的完整激活周期。这些模拟揭示了受体激活和 G 蛋白偶联的中间状态，这些状态与实验结构相辅相成。这些见解有助于解释不同配体（激动剂、部分激动剂、拮抗剂）如何稳定某些受体状态，这对于设计具有所需效力和信号特征的药物至关重要。

Improved Binding Affinity Predictions: A long-standing goal in drug discovery is to accurately predict how strongly a drug binds its target. In recent years, MD-based free energy methods have matured and saw successful application in high-impact studies. For instance, researchers in a 2022 Nature study combined ultra-large library docking with MD pose refinement and free-energy perturbation (FEP) calculations​
pmc.ncbi.nlm.nih.gov
. Starting from ~75 million compounds docked to a 5-HT<sub>2A</sub> serotonin receptor model, they ran MD on top candidates, identified stable binding poses, and used FEP to predict binding affinities​
pmc.ncbi.nlm.nih.gov
​
pmc.ncbi.nlm.nih.gov
. This approach helped prioritize novel agonists that were later confirmed experimentally to have potent and functionally selective activity in vitro and in vivo​
pmc.ncbi.nlm.nih.gov
. The success demonstrates how MD-driven affinity calculations can guide lead optimization by quantitatively ranking candidates.改进的亲和力预测：药物发现中的一个长期目标是准确预测药物与其靶标结合的强度。近年来，基于 MD 的自由能方法已经成熟，并在高影响力研究中取得了成功应用。例如，2022 年《自然》杂志的一项研究中，研究人员结合了超大型库对接、MD 构象优化和自由能扰动（FEP）计算。从约 7500 万个与 5-HT<sub>2A</sub>血清素受体模型对接的化合物开始，他们对候选化合物进行了 MD 模拟，确定了稳定的结合构象，并使用 FEP 预测了结合亲和力。这种方法有助于优先考虑后来经实验证实具有体外和体内强大且功能选择性活性的新型激动剂。这一成功展示了 MD 驱动的亲和力计算如何通过定量排序候选化合物来指导先导化合物的优化。

Integration with Screening and Structure-Based Design: The synergy of MD with experimental and computational screening has led to tangible drug discovery outcomes. In the 5-HT<sub>2A</sub> example above, MD was integrated into a bespoke virtual screening pipeline to discover drug candidates with anti-depressant activity​
pmc.ncbi.nlm.nih.gov
. Likewise, MD simulations are routinely used to refine ligand poses from docking, account for target flexibility, and suggest modifications for better fit. This integrated approach is increasingly common in pharma research​
pnas.org
. Another frontier is using MD to evaluate drug resistance mutations or drug off-target effects early, though those applications are just emerging.整合筛选和基于结构的药物设计：分子动力学（MD）与实验和计算筛选的协同作用导致了有形的药物发现成果。在上面的 5-HT<sub>2A</sub>示例中，MD 被整合到一个定制的虚拟筛选流程中，以发现具有抗抑郁活性的药物候选物。同样，MD 模拟通常用于从对接中优化配体姿态，考虑靶点的灵活性，并提出改进以更好地匹配。这种集成方法在制药研究中越来越普遍。另一个前沿是使用 MD 早期评估药物耐药突变或药物脱靶效应，尽管这些应用刚刚兴起。

Practical Implications – from Computer to Clinic: Notably, MD predictions have started influencing real-world drug development decisions. For example, during the COVID-19 pandemic, all-atom MD simulations of the SARS-CoV-2 spike protein (including its glycan shield) elucidated how host-cell receptor (ACE2) glycans modulate virus binding​
pubmed.ncbi.nlm.nih.gov
​
pubmed.ncbi.nlm.nih.gov
. These simulations showed that certain human ACE2 glycosylation sites can either hinder or strengthen spike attachment, pinpointing a glycan that blocks the viral interface and another that reinforces binding​
pubmed.ncbi.nlm.nih.gov
​
pubmed.ncbi.nlm.nih.gov
. Such findings guided the design of neutralizing antibodies and fusion inhibitors targeting the virus​
pubmed.ncbi.nlm.nih.gov
. This example underscores how MD, by capturing biologically relevant motions and interactions, is directly informing therapeutic design against emerging diseases.实际应用——从计算机到诊所：值得注意的是，MD 预测已经开始影响现实世界的药物开发决策。例如，在 COVID-19 大流行期间，SARS-CoV-2 刺突蛋白（包括其糖基保护层）的全原子 MD 模拟阐明了宿主细胞受体（ACE2）的糖基如何调节病毒结合。这些模拟表明，某些人类 ACE2 糖基化位点可以阻碍或增强刺突蛋白的附着，确定了一种阻断病毒界面的糖基和另一种增强结合的糖基。这些发现指导了针对病毒的中和抗体和融合抑制剂的研发。这个例子突显了 MD 通过捕捉生物相关的运动和相互作用，如何直接为新兴疾病的药物治疗提供信息。

(Briefly, algorithmic advances like “supervised MD” protocols have also accelerated ligand-binding simulations. These methods steer ligand molecules into the binding site efficiently, allowing rapid mapping of binding pathways for drug targets such as GPCRs​简而言之，像“监督 MD”协议这样的算法进步也加速了配体结合模拟。这些方法有效地将配体分子引导到结合位点，允许快速绘制药物靶点如 GPCR 的绑定途径图。
elifesciences.org
. However, the core value of MD in drug discovery remains its ability to reveal what static structures miss – the dynamic dance of drug and target.)然而，MD 在药物发现中的核心价值仍然是其揭示静态结构所遗漏的内容——药物与靶点的动态舞蹈。

Molecular Dynamics in Materials Science分子动力学在材料科学中的应用

In materials science, MD simulations have enabled atomistic exploration of complex material behaviors – from the self-assembly of nanostructures to mechanical deformation and transport phenomena. Recent advances emphasize scaling up simulations (in system size and timescale) and improving realism, thereby bridging the gap between simulations and practical materials design:在材料科学中，分子动力学模拟使人们能够对复杂材料行为进行原子级别的探索——从纳米结构的自组装到机械变形和传输现象。最近的研究进展强调扩大模拟规模（在系统大小和时间尺度上）并提高现实性，从而弥合了模拟与实际材料设计之间的差距：

Machine-Learned Potentials for Scale and Accuracy: A breakthrough in the last five years is the rise of machine-learned interatomic potentials. These are AI-trained force fields that approach quantum-level accuracy while remaining fast enough for large systems​
pubmed.ncbi.nlm.nih.gov
. As reviewed by Friederich et al. (2021) in Nature Materials, modern machine learning potentials (e.g. neural network or Gaussian approximation potentials) can reproduce the precision of DFT calculations at a fraction of the cost​
pubmed.ncbi.nlm.nih.gov
. This allows researchers to simulate previously intractable systems – such as battery materials, catalysts, or complex alloys – with millions of atoms or longer timescales, without sacrificing accuracy. Applications span from organic and biomolecular systems to inorganic crystals and surfaces​
pubmed.ncbi.nlm.nih.gov
. This development is paving the way for fully computational materials design, where researchers screen and optimize materials in silico before any lab synthesis​
pubmed.ncbi.nlm.nih.gov
.机器学习的尺度与精度：过去五年的一项突破是机器学习原子间势的兴起。这些是经过 AI 训练的力场，在保持足够快速以适应大系统的同时，接近量子级精度。正如 Friederich 等人（2021）在《自然材料》杂志中回顾的那样，现代机器学习势（例如神经网络或高斯近似势）可以在成本的一小部分内重现 DFT 计算的精度。这使得研究人员能够以前无法处理的系统——如电池材料、催化剂或复杂合金——进行数百万原子或更长时间尺度的模拟，而不牺牲精度。应用范围从有机和生物分子系统到无机晶体和表面。这一发展正在为完全计算材料设计铺平道路，研究人员在实验室合成之前，在计算机上筛选和优化材料。

Understanding Nanoscale Phenomena (Friction, Fluids, and Beyond): MD has provided key insights into phenomena that are difficult to capture experimentally. A prominent example is nanotribology – friction at the molecular level. Non-equilibrium MD simulations have been used to study ionic liquid lubricants confined between surfaces under electric fields​
pubmed.ncbi.nlm.nih.gov
. Such simulations, highlighted in a 2022 Nature Materials review, revealed how applying an electric bias can toggle friction on and off by reorganizing ions at the interface​
pubmed.ncbi.nlm.nih.gov
. The ability to “tune” friction via electric fields (electrotunable friction) was demonstrated through MD and later verified with atomic force microscopy​
pubmed.ncbi.nlm.nih.gov
. This insight opens the door to smart lubricants for advanced coatings and MEMS devices. More broadly, MD studies of confined fluids, surface coatings, and nanocomposites have deepened our understanding of thermal transport, adhesion, and wear at the atomic scale – knowledge essential for designing durable materials and nanomachines.纳米尺度现象（摩擦、流体等）：分子动力学（MD）为难以通过实验捕捉的现象提供了关键见解。一个突出的例子是纳米摩擦学——分子层面的摩擦。非平衡 MD 模拟已被用于研究在电场下表面之间受限的离子液体润滑剂。2022 年《自然材料》综述中突出显示的此类模拟揭示了通过重新组织界面处的离子，施加电偏压可以切换摩擦的开关。通过 MD 演示了通过电场“调节”摩擦（电可调摩擦）的能力，并通过原子力显微镜进行了验证。这一见解为智能润滑剂在先进涂层和 MEMS 设备中的应用打开了大门。更广泛地说，对受限流体、表面涂层和纳米复合材料的 MD 研究加深了我们对原子尺度热传输、粘附和磨损的理解——这些知识对于设计耐久材料和纳米机械至关重要。

Materials Discovery and Design: High-impact work has shown that MD simulations are not just explanatory tools but can drive the discovery of new materials. For instance, researchers now combine high-throughput MD with data-driven models to screen candidate materials for desirable properties (like ion conductivity, mechanical strength, or catalytic activity). Physics-aware machine learning models, trained on MD simulation data, can predict how changes in composition or structure will affect material performance​
pubmed.ncbi.nlm.nih.gov
. This approach has been applied to design better alloys and polymer electrolytes by rapidly evaluating many variants in silico. In one demonstration, an automated pipeline used atomistic simulations to evaluate defect dynamics in alloys, guiding the optimization of compositions for improved high-temperature strength​
pnas.org
. Such examples highlight a shift toward “MD-in-the-loop” materials engineering, where simulation results iteratively inform experimental choices. The practical implication is faster development of materials like batteries, catalysts, and nanomaterials, as computational screening narrows down the vast design space to the most promising candidates.材料发现与设计：高影响力工作表明，分子动力学模拟不仅是一种解释工具，还能推动新材料发现的进程。例如，研究人员现在将高通量分子动力学与数据驱动模型相结合，筛选具有理想性质（如离子导电性、机械强度或催化活性）的候选材料。在分子动力学模拟数据上训练的物理感知机器学习模型可以预测组成或结构的变化如何影响材料性能。这种方法已被应用于通过在计算机上快速评估许多变体来设计更好的合金和聚合物电解质。在一个演示中，一个自动化的管道使用原子模拟来评估合金中的缺陷动力学，指导优化成分以提高高温强度。这些例子突显了向“MD-in-the-loop”材料工程的转变，在这种工程中，模拟结果迭代地指导实验选择。 实际意义在于，计算筛选缩小了设计空间，加快了电池、催化剂和纳米材料等材料的发展。

Capturing Complex Chemical Processes: Recent MD advancements also extend to reactive simulations and multi-scale modeling in materials science. Reactive MD force fields (and even ab initio MD in short bursts) allow simulation of chemical reactions within materials – for example, the lithiation of battery anodes or the curing of polymer resins – providing atomic detail on reaction pathways and intermediates. While not the primary focus of this review, these techniques have yielded insights published in chemistry journals (e.g., understanding the SN1/SN2 mechanisms in organic reactions via ab initio MD​
pubmed.ncbi.nlm.nih.gov
). In materials contexts, similar approaches help unravel how catalysts operate or how materials degrade in harsh conditions, informing the design of more robust materials. Furthermore, coupling MD with mesoscale models has enabled the study of grain boundary dynamics, crystal growth, and even predictions of glass formation, connecting atomic-level events to bulk material properties in silico.捕捉复杂化学过程：最近的分子动力学（MD）进展也扩展到反应模拟和材料科学中的多尺度建模。反应性 MD 力场（甚至短脉冲的从头算 MD）允许在材料内模拟化学反应——例如，电池阳极的锂化或聚合物树脂的固化——提供反应途径和中间体的原子级细节。虽然这不是本综述的主要焦点，但这些技术已在化学期刊上发表了见解（例如，通过从头算 MD 理解有机反应中的 SN1/SN2 机制 PUBMED.NCBI.NLM.NIH.GOV）。在材料背景下，类似的方法有助于揭示催化剂的工作原理或材料在恶劣条件下的降解，从而为更坚固的材料设计提供信息。此外，将 MD 与介观模型耦合已使研究晶界动力学、晶体生长甚至玻璃形成的预测成为可能，将原子级事件与材料宏观性质在计算机上联系起来。

Conclusion结论

Molecular dynamics has evolved into a powerful, versatile tool at the intersection of computational science and practical application. In drug discovery, state-of-the-art MD simulations are identifying new binding sites, explaining ligand efficacy, and quantitatively ranking drug candidates – contributions that help bring better drugs to market faster. In materials science, MD (augmented by machine learning) is unlocking the behaviors of complex materials and guiding the design of new ones with unprecedented efficiency. These advances, reported in journals like Nature, Science, PNAS, JACS, Nature Materials, and Nature Chemistry, underscore a common theme: by capturing the motion of atoms, MD provides insights that static theories or experiments alone often cannot. As algorithms and hardware continue to improve, one can expect MD to play an even greater role in future breakthroughs – from tailoring molecule-specific medicines to inventing materials that meet the pressing needs of technology and society.分子动力学已成为计算科学和实际应用交汇处的一种强大、多功能的工具。在药物发现中，最先进的 MD 模拟正在识别新的结合位点，解释配体效力，并对药物候选物进行定量排名——这些贡献有助于更快地将更好的药物推向市场。在材料科学中，MD（借助机器学习）正在揭示复杂材料的特性，以前所未有的效率指导新材料的开发。这些进步在《自然》、《科学》、《PNAS》、《JACS》、《自然材料》和《自然化学》等期刊上得到报道，强调了一个共同的主题：通过捕捉原子的运动，MD 提供了静态理论或实验单独往往无法提供的见解。随着算法和硬件的不断改进，人们可以期待 MD 在未来的突破中扮演更加重要的角色——从定制特定分子的药物到发明满足技术和社会迫切需求的新材料。

References: (Key recent publications are cited in-line, using the format 【citation】 to refer to the source.)参考文献：（近期关键出版物在正文中以【引用】格式引用，以指明来源。）

Gervasio et al., Acc. Chem. Res. (2020) – on cryptic binding site discovery via MD​Gervasio 等人，《化学研究通讯》（2020）——关于通过分子动力学模拟发现隐秘结合位点
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Lückmann et al., PNAS (2019) – MD-guided discovery of an allosteric agonist for a GPCR (FFAR1)​Lückmann 等人，《美国国家科学院院刊》（2019 年）——基于 MD 引导的 G 蛋白偶联受体（FFAR1）变构激动剂的发现
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Li et al., PNAS (2022) – development of Su-GaMD to capture full GPCR activation mechanism​李等人，PNAS（2022）——Su-GaMD 的开发以捕捉完整的 GPCR 激活机制
pnas.orgPNAS.ORG
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Kaplan et al., Nature (2022) – integration of ultra-large docking, MD, and FEP for 5-HT<sub>2A</sub> agonist discovery​Kaplan 等人，《自然》（2022）——超大型对接、分子动力学和 FEP 的整合用于 5-HT2A 激动剂发现
pmc.ncbi.nlm.nih.govPMC.NCBI.NLM.NIH.GOV
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Mehdipour & Hummer, PNAS (2021) – MD study of ACE2 glycosylation effects on SARS-CoV-2 spike binding​梅迪波尔与胡默，PNAS（2021）——ACE2 糖基化对 SARS-CoV-2 刺突蛋白结合的 MD 研究
pubmed.ncbi.nlm.nih.gov
​
pubmed.ncbi.nlm.nih.gov
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Friederich et al., Nat. Mater. (2021) – review of machine-learned potentials for fast and accurate MD in materials science​弗里德里希等人，自然材料（2021）——关于材料科学中快速和准确分子动力学模拟的机器学习势能综述
pubmed.ncbi.nlm.nih.gov
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pubmed.ncbi.nlm.nih.gov
.

Bresme et al., Nat. Mater. (2022) – perspective on electrotunable friction, highlighting insights from non-equilibrium MD​Bresme 等人，《自然·材料》（2022）——关于电可调摩擦的展望，强调非平衡分子动力学中的见解
pubmed.ncbi.nlm.nih.gov
.

(Additional sources as cited above within the text​上述文本中引用的额外来源
elifesciences.org
​
pubmed.ncbi.nlm.nih.gov
, etc., provide further reading on specific methods and applications.)等，提供有关特定方法和应用的进一步阅读材料。

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