Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

{"title":"Fragme∩t: An Open-Source Framework for Multiscale Quantum Chemistry Based on Fragmentation","authors":"Dustin R. Broderick,&nbsp;Paige E. Bowling,&nbsp;Chance Brandt,&nbsp;Sigrún Childress,&nbsp;Joshua Shockey,&nbsp;Jonah Higley,&nbsp;Haden Dickerson,&nbsp;Syed Sharique Ahmed,&nbsp;John M. Herbert","doi":"10.1002/wcms.70058","DOIUrl":"https://doi.org/10.1002/wcms.70058","url":null,"abstract":"<p>Fragment-based quantum chemistry offers a means to circumvent the nonlinear computational scaling of conventional electronic structure calculations, by partitioning a large calculation into smaller subsystems then considering the many-body interactions between them. Variants of this approach have been used to parameterize classical force fields and machine learning potentials, applications that benefit from interoperability between quantum chemistry codes. However, there is a dearth of software that provides interoperability yet is purpose-built to handle the combinatorial complexity of fragment-based calculations. To fill this void we introduce “<span>Fragme∩t</span>”, an open-source software application that provides a tool for community validation of fragment-based methods, a platform for developing new approximations, and a framework for analyzing many-body interactions. <span>Fragme∩t</span> includes algorithms for automatic fragment generation and structure modification, and for distance- and energy-based screening of the requisite subsystems. Checkpointing, database management, and parallelization are handled internally and results are archived in a portable database. Interfaces to various quantum chemistry engines are easy to write and exist already for Q-Chem, PySCF, xTB, Orca, CP2K, MRCC, Psi4, NWChem, GAMESS, and MOPAC. Applications reported here demonstrate parallel efficiencies around 96% on more than 1000 processors but also showcase that the code can handle large-scale protein fragmentation using only workstation hardware, all with a codebase that is designed to be usable by non-experts. <span>Fragme∩t</span> conforms to modern software engineering best practices and is built upon well established technologies including Python, SQLite, and Ray. The source code is available under the Apache 2.0 license.</p><p>This article is categorized under:\u0000\u0000 </p>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"15 6","pages":""},"PeriodicalIF":27.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://wires.onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.70058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145695031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coarse-Grained Modeling of Electrostatic Interactions in Chromatin","authors":"Nikolay Korolev,&nbsp;Tiedong Sun,&nbsp;Alexander P. Lyubartsev,&nbsp;Lars Nordenskiöld","doi":"10.1002/wcms.70059","DOIUrl":"https://doi.org/10.1002/wcms.70059","url":null,"abstract":"<p>The double-helical DNA of large eukaryotic genomes is tightly compacted within the tiny cell nucleus as a DNA–protein complex, chromatin. The universal elements of chromatin, nucleosome core particles (NCPs, 147 base pairs of DNA wrapped around an octamer of histone proteins), are connected by linker DNA of variable lengths into nucleosome arrays, which fold into various and dynamic higher-order structures. Since DNA is a highly negatively charged polyelectrolyte, electrostatic interactions of DNA with positively charged histones, other charged nuclear proteins, as well as with monovalent and multivalent cations, contributes decisively to the formation and folding of nucleosome arrays. The dimensions and timescales of cellular chromatin states and transformations necessitate a multiscale coarse-graining (CG) approach to understand their properties through computational modeling. In this review, we highlight the importance of electrostatics for NCP interactions and nucleosome fiber folding in vitro and in vivo, and argue that the inclusion of explicit ions is indispensable for accurate CG modeling of chromatin structure and dynamics. A summary of the existing CG mapping and force field setups is provided. A brief account of CG modeling studies in which salt dependency is approximated by the Debye–Hückel treatment is given. The primary focus is on the presentation of results from papers that include explicit monovalent and multivalent ionic species in CG simulations of nucleosomes and nucleosome arrays. Finally, we underline perspectives and challenges for future multiscale computational modeling of chromatin.</p><p>This article is categorized under:\u0000\u0000 </p>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"15 6","pages":""},"PeriodicalIF":27.0,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://wires.onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.70059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145619129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Principal Component Analysis of Molecular Dynamic Trajectories: Concepts, Tools, and Applications","authors":"Danilo Roccatano","doi":"10.1002/wcms.70060","DOIUrl":"https://doi.org/10.1002/wcms.70060","url":null,"abstract":"<p>Principal component analysis (PCA) is a central tool for extracting essential information from complex datasets and has become widely used in the study of dynamical systems across disciplines. Its interdisciplinary relevance spans physics, chemistry, biology, computer science, and applied mathematics, where PCA and related approaches serve as gateways to understanding structure–function relationships, emergent behavior, and data-driven modeling. In the theoretical study of biomolecular systems using molecular dynamics (MD) simulations method, PCA filters high-dimensional trajectories into a reduced set of collective motions that elucidate conformational transitions and functional mechanisms. PCA provides an intuitive framework to connect statistical variance with dominant dynamical modes, a concept that extends naturally to the atomic scale of biomolecules. Modern developments integrate PCA with time-lagged methods, Markov state models, nonlinear dimensionality reduction, and machine learning techniques. These advances capture slow modes, rare events, and nonlinear manifolds, enriching the understanding of MD simulations results. A variety of computational packages now provide PCA-based analyses, supporting workflows from raw trajectory processing to visualization of free-energy landscapes and structural conformations. Applications range from probing peptide folding and protein domain motions to exploring collective dynamics in large assemblies. Since their first application more than 30 years ago to MD simulation, PCA-based methods continue to enhance the ability to analyze complex dynamical systems, offering a unifying statistical perspective that connects molecular simulations with interdisciplinary approaches to high-dimensional data analysis.</p><p>This article is categorized under:\u0000\u0000 </p>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"15 6","pages":""},"PeriodicalIF":27.0,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://wires.onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.70060","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145619065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Weighted Ensemble Simulation: Advances in Methods, Software, and Applications","authors":"Lillian T. Chong,&nbsp;Daniel M. Zuckerman","doi":"10.1002/wcms.70055","DOIUrl":"https://doi.org/10.1002/wcms.70055","url":null,"abstract":"<div>\u0000 \u0000 <p>For more than two decades, weighted ensemble (WE) path sampling strategies have enabled the simulation of pathways for rare events—or barrier-crossing processes—with significantly less computing cost than conventional simulations, all while preserving rigorous kinetics. This review highlights recent advances in WE methods and software, including tools for mechanistic analysis of path ensembles and efficient estimation of rates. We showcase successful WE applications across a wide range of condensed-phase processes, such as hybrid quantum mechanics/molecular mechanics (QM/MM) simulations of microsecond-timescale chemical reactions, and atomistic simulations of slower processes on the millisecond to seconds timescale. These applications span drug membrane permeation, ligand unbinding, and the large-scale opening of the SARS-CoV-2 spike protein. We also discuss the current limitations and key challenges facing WE strategies, which have yet to reach their full potential.</p>\u0000 <p>This article is categorized under:\u0000\u0000 </p><ul>\u0000 \u0000 <li>Molecular and Statistical Mechanics &gt; Molecular Dynamics and Monte-Carlo Methods</li>\u0000 \u0000 <li>Software &gt; Simulation Methods</li>\u0000 \u0000 <li>Structure and Mechanism &gt; Computational Biochemistry and Biophysics</li>\u0000 </ul>\u0000 </div>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"15 6","pages":""},"PeriodicalIF":27.0,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145530154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Feature-Based Chemical Similarity to Chemical Language Models—A Paradigm Shift in Computer-Aided Molecular Design and Property Predictions","authors":"Arkaprava Banerjee,&nbsp;Supratik Kar,&nbsp;Kunal Roy,&nbsp;Grace Patlewicz,&nbsp;Imran Shah,&nbsp;Panagiotis G. Karamertzanis,&nbsp;Giuseppina Gini,&nbsp;Emilio Benfenati","doi":"10.1002/wcms.70057","DOIUrl":"https://doi.org/10.1002/wcms.70057","url":null,"abstract":"<div>\u0000 \u0000 <p>This review provides a comprehensive overview of the paradigm shift for computer-aided molecular design and property predictions from similarity-based modeling, including quantitative structure–activity/property relationship (QSAR/QSPR), read-across, read-across structure–activity relationship (RASAR), and pharmacophore mapping to sequence-based chemical language models (CLMs) using deep learning techniques. Starting with multiple methods of chemical structure and latent chemical space representations and touching the molecular descriptor- and fingerprint-based classical type modeling, this review introduces string-based deep learning models involving techniques like recurrent neural networks (RNNs) with long short-term memory (LSTM) and other architectures such as variational autoencoder (VAE), attention models, and generative adversarial networks (GANs). The basics of more efficient transformer models are also discussed. The problem-solving of training with scarce data using transfer learning, data augmentation, and natural-product-inspired training is analyzed. The applications of CLMs in the de novo design of small molecules of medicinal interest, enzymes, peptides, and multitask agents, the predictions of properties of drug candidates, and activity cliffs are presented. The applications of CLMs in materials science and predictive toxicology are also mentioned. We discuss the limitations of feature-based modeling approaches confined to a restricted feature space. In contrast, CLMs lack specific insights into aspects like SARs, bioisosteric replacements, synthesizability, and so forth, which collectively hinder their regulatory acceptance and acceptance by synthetic chemists. This review concludes that cheminformaticians need to utilize two complementary approaches, where factors like simplicity, reproducibility, and regulatory acceptability may prompt the use of feature-based approaches while aiming for higher accuracy and generating novel molecules may drive toward adopting CLMs.</p>\u0000 <p>This article is categorized under:\u0000\u0000 </p><ul>\u0000 \u0000 <li>Data Science &gt; Chemoinformatics</li>\u0000 \u0000 <li>Structure and Mechanism &gt; Computational Biochemistry and Biophysics</li>\u0000 \u0000 <li>Software &gt; Molecular Modeling</li>\u0000 </ul>\u0000 </div>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"15 6","pages":""},"PeriodicalIF":27.0,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drug–Drug Interaction Prediction: Paradigm Shifts, Key Bottlenecks, and Future Directions","authors":"Xiaoqing Ru,&nbsp;Zhen Li,&nbsp;Leyi Wei,&nbsp;Yuanan Liu,&nbsp;Quan Zou","doi":"10.1002/wcms.70056","DOIUrl":"https://doi.org/10.1002/wcms.70056","url":null,"abstract":"<div>\u0000 \u0000 <p>Polypharmacy has become a routine practice in modern medicine, yet the risks of drug–drug interactions (DDIs) remain a critical challenge for patient safety. Given the vast number of possible drug combinations and the impracticality of exhaustive clinical testing, computational approaches have become indispensable for DDI prediction. Over the past 15 years, the field has shifted from handcrafted, similarity-based models to deep learning and graph neural networks (GNNs). Prediction tasks have also expanded from binary classification to multi-class, multi-label, cold-start, and higher-order settings. These reflect an emerging paradigm in both methodology and scope. Yet critical bottlenecks remain. Data sparsity, unreliable negatives, class imbalance, and source heterogeneity undermine robustness; models still struggle with generalization to unseen drugs, with mechanistic interpretability, and with capturing asymmetric or higher-order interactions. These limitations continue to impede translation into clinical and regulatory practice. In this Advanced Review, we critically assess methodological evolution, benchmark datasets, and emerging paradigms, including GNNs, large language models (including multimodal extensions), and generative AI, and examine their promises and limitations. We argue that next-generation progress hinges on unified multimodal and mechanism-aware frameworks, strategies for robust learning under cold-start and long-tail scenarios, and the integration of causal inference with generative approaches to enhance interpretability. By synthesizing past advances with forward-looking perspectives, this review outlines strategic pathways for accelerating the transition of DDI prediction toward intelligent, interpretable, and clinically actionable solutions.</p>\u0000 <p>This article is categorized under:\u0000\u0000 </p><ul>\u0000 \u0000 <li>Data Science &gt; Artificial Intelligence/Machine Learning</li>\u0000 \u0000 <li>Data Science &gt; Chemoinformatics</li>\u0000 \u0000 <li>Molecular and Statistical Mechanics &gt; Molecular Interactions</li>\u0000 </ul>\u0000 </div>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"15 6","pages":""},"PeriodicalIF":27.0,"publicationDate":"2025-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145476432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Path Integral-Free Energy Perturbation (PI-FEP) Simulations: Kinetic Isotope Effects of Proton/Deuteron Transfer Reactions in Aqueous Solution","authors":"Jiali Gao,&nbsp;Gavin Shuai Huang,&nbsp;Amber Simon,&nbsp;Elinor Caballero,&nbsp;Kai Chen,&nbsp;Mikayla Z. Fahrenbruch,&nbsp;Dallin Fairbourn,&nbsp;Ian Harreschou,&nbsp;Skyler Kauffman,&nbsp;Calvin Thoma,&nbsp;Marissa D. Zamora","doi":"10.1002/wcms.70053","DOIUrl":"https://doi.org/10.1002/wcms.70053","url":null,"abstract":"<p>We present a tutorial review of the theoretical background and a step-by-step computational procedure for determining kinetic isotope effects (KIEs) of chemical reactions in aqueous solution. The method combines path integral and free energy perturbation (PI-FEP) simulations to directly yield the ratio of the partition functions between different isotopic reactions. This review is the result of collaborative work in a Computational Chemistry course at the University of Minnesota, where two intramolecular proton-transfer reactions were given as classroom exercises. Through this study, we wish to accomplish three main goals: (i) determination of nuclear quantum effects and quantum-mechanical potentials of mean force (QM-PMF), (ii) computation of primary KIE using PI-FEP simulations, and (iii) an understanding of solvent effects on proton-transfer reactions in water. Analyses of computational results provide insights into substituent effects on chemical reactivity, solvent effects on reaction rate, nuclear quantum effects on free energy barrier, and KIEs on transition state. The theory and computational procedure for determining KIE can be directly used to study chemical reactions in solutions and enzymatic processes with two publicly available software packages (CHARMM and QBICS).</p><p>This article is categorized under:\u0000\u0000 </p>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"15 6","pages":""},"PeriodicalIF":27.0,"publicationDate":"2025-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://wires.onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.70053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145476431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements in Large Language Models (LLMs): Empowering Drug Discovery","authors":"Bosheng Song,&nbsp;Xiaowen Li,&nbsp;Xiuxiu Chao,&nbsp;Li Wang,&nbsp;Yiping Liu,&nbsp;Zhen Xia,&nbsp;Dongsheng Cao,&nbsp;Xiangzheng Fu","doi":"10.1002/wcms.70054","DOIUrl":"https://doi.org/10.1002/wcms.70054","url":null,"abstract":"<div>\u0000 \u0000 <p>In recent years, the emergence of foundation models such as GPT and BERT has driven rapid advancements in large-scale artificial intelligence, with large language models (LLMs) becoming especially transformative. These models have shown tremendous potential in accelerating drug discovery and development, offering new tools to enhance human health and medicine. This paper provides a focused review of the application of LLMs in five key areas of drug discovery: disease-target prediction, lead compound design and optimization, drug-target interaction prediction, molecular property prediction, and drug–drug interaction prediction. Additionally, we examine the current limitations of LLMs in these domains and discuss potential strategies to address them. Finally, we summarize the progress to date and outline promising directions for future research and development in this rapidly evolving field.</p>\u0000 <p>This article is categorized under:\u0000\u0000 </p><ul>\u0000 \u0000 <li>Data Science &gt; Computer Algorithms and Programming</li>\u0000 \u0000 <li>Data Science &gt; Artificial Intelligence/Machine Learning</li>\u0000 \u0000 <li>Molecular and Statistical Mechanics &gt; Molecular Interactions</li>\u0000 </ul>\u0000 </div>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"15 6","pages":""},"PeriodicalIF":27.0,"publicationDate":"2025-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145476429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to “ByteQC: GPU-Accelerated Quantum Chemistry Package for Large-Scale Systems”","authors":"","doi":"10.1002/wcms.70052","DOIUrl":"10.1002/wcms.70052","url":null,"abstract":"<p>Z. Guo, Z. Huang, Q. Chen, et al. “ByteQC: GPU-Accelerated Quantum Chemistry Package for Large-Scale Systems,” <i>Wiley Interdisciplinary Reviews: Computational Molecular Science</i> 15 (2025): e70034, https://doi.org/10.1002/wcms.70034</p><p>In the originally published article, the affiliations of the sixth and ninth authors were incorrectly listed, and one of the affiliations of the ninth author was missing from the affiliation list.</p><p>The correct affiliations of the authors are as follows:</p><p>Hung Q. Pham³</p><p>³ByteDance Seed, San Jose, California, USA</p><p>Ji Chen^4,5,6</p><p>⁴School of Physics, Peking University, Beijing, China</p><p>⁵Interdisciplinary Institute of Light-Element Quantum Materials and Research Center for Light-Element Advanced Materials, Peking University, Beijing, China</p><p>⁶Frontiers Science Center for Nano-Optoelectronics, Peking University, Beijing, China</p><p>We apologize for this error.</p>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"15 5","pages":""},"PeriodicalIF":27.0,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://wires.onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.70052","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145366796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aptamers Meet Structural Bioinformatics, Computational Chemistry, and Artificial Intelligence","authors":"Gabriela da Rosa,&nbsp;Mauro de Castro,&nbsp;Víctor Miguel García Velásquez,&nbsp;Santiago Pintos,&nbsp;Jimena Benedetto,&nbsp;Leandro Grille,&nbsp;Sofia Valla,&nbsp;Luis Marat Alvarez Salas,&nbsp;Victoria Calzada,&nbsp;Pablo D. Dans","doi":"10.1002/wcms.70050","DOIUrl":"10.1002/wcms.70050","url":null,"abstract":"<div>\u0000 \u0000 <p>Aptamers—short single-stranded DNA or RNA—are the latest biomolecules to fall within reach of powerful structure-prediction pipelines that blend bioinformatics, computational chemistry, and artificial intelligence. These tools now enable high-throughput exploration of aptamer conformational landscapes, a prerequisite for rational design and optimization of their exceptional target affinity and specificity. Next-generation sequencing has democratized library analysis, allowing any laboratory to handle millions of variants. Hybrid workflows currently offer the most reliable secondary and tertiary structure models, and explicit treatment of conformational flexibility is proving indispensable for mapping binding-competent states. Yet every predictive tier—from classic free-energy minimization to deep learning—still underrepresents chemically modified nucleotides, the very substitutions that grant therapeutic aptamers nuclease resistance and pharmacokinetic longevity. Capturing the structural and dynamical consequences of these modifications remains the key unsolved problem. Progress, therefore, hinges on two fronts: richer parameterization and training data that encompass modified bases, and tighter coupling of <i>in silico</i> screens with biophysical and structural validation. Bridging these gaps will convert the current wave of computational advances into clinically relevant aptamer-based drugs ready to be delivered to the patients.</p>\u0000 <p>This article is categorized under:\u0000\u0000 </p><ul>\u0000 \u0000 <li>Structure and Mechanism &gt; Molecular Structures</li>\u0000 \u0000 <li>Data Science &gt; Computer Algorithms and Programming</li>\u0000 \u0000 <li>Data Science &gt; Artificial Intelligence/Machine Learning</li>\u0000 </ul>\u0000 </div>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"15 5","pages":""},"PeriodicalIF":27.0,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145317202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}