Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

{"title":"IMPACT Framework: Establishing Global Standards for Artificial Intelligence Implementation, Methodology, and Translation in Drug Discovery","authors":"Amit Gangwal,&nbsp;Antonio Lavecchia","doi":"10.1002/wcms.70072","DOIUrl":"10.1002/wcms.70072","url":null,"abstract":"<p>Artificial intelligence (AI) is reshaping drug discovery by accelerating timelines and reducing costs, yet its impact remains constrained by a persistent gap between computational promise and translational delivery. This gap stems from upstream preclinical failures, including weak target validation, biologically irrelevant models, and insufficient accountability for overstated methodological claims that contribute to late-stage attrition. The Implementation, Methodology, Productivity, Assessment, Collaboration, Translation (IMPACT) framework addresses these root causes by establishing global standards that reinforce biological grounding, methodological credibility, and equitable collaboration. Implementation emphasizes Findable, Accessible, Interoperable, and Reusable (FAIR)-compliant datasets, standardized vocabularies, and clear gradients of AI involvement from assisted to fully AI-driven workflows. Methodology prioritizes reproducibility through model cards, containerized environments, and transparent reporting to support robust models. Productivity aligns AI efforts with urgent therapeutic priorities, including rare diseases, antimicrobial resistance, drug repurposing, and natural-product discovery. Assessment promotes rigorous benchmarking, blind validation, and uncertainty quantification, drawing on the long-established CASP model as a historical gold standard while critically examining emerging initiatives such as CACHE and Polaris Hub, which remain comparatively recent and evolving. Collaboration leverages federated learning, pre-competitive consortia, and interdisciplinary teams integrating AI specialists with domain experts. Translation ensures outputs are explainable, clinically relevant, ethically aligned, and regulatory-ready, consistent with emerging frameworks such as the FDA Draft Guidance on AI in Drug Development and the EU AI Act. By integrating technical standards with operational governance mechanisms, IMPACT provides a structured pathway toward transparent and translationally reliable AI-driven drug discovery.</p><p>This article is categorized under:\u0000\u0000 </p>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"16 2","pages":""},"PeriodicalIF":27.0,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://wires.onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.70072","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708433","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":"Formulation and Advancement of Hierarchically Correlated Orbital Functional Theory","authors":"Ting Zhang,&nbsp;Yi-Fan Yao,&nbsp;Wenna Ai,&nbsp;Neil Qiang Su","doi":"10.1002/wcms.70070","DOIUrl":"10.1002/wcms.70070","url":null,"abstract":"<div>\u0000 \u0000 <p>Functional theories reformulate the many-electron problem by expressing electronic properties as functionals of reduced quantities, providing efficient alternatives to wave function-based correlation methods. Kohn-Sham density functional theory (KS-DFT) and reduced density matrix functional theory (RDMFT) exemplify this philosophy but remain limited by their single-determinant nature and numerical complexity, respectively. This review presents hierarchically correlated orbital functional theory (HCOFT), a unified framework developed to overcome these limitations. By extending orbitals into tunable hypercomplex spaces and deriving hierarchically correlated orbitals (HCOs) with fractional occupations through Clifford algebra, HCOFT establishes the corresponding variational foundation and a continuous dimensional hierarchy that spans KS-DFT, RDMFT, and the intermediate 1-HCOFT—a third formal functional theory featuring paired HCOs that naturally capture strong correlation while maintaining computational stability. Further advances, including the explicit-by-implicit scheme for stable occupation optimization, the coupled optimization strategy for accelerated convergence through simultaneous orbital and occupation updates, and the development of short-range screened, occupation-dependent orbital functionals for balanced treatment of dynamical and strong correlation, further strengthen the practical applicability of HCOFT. By integrating mathematical rigor, algorithmic efficiency, and a flexible platform for functional construction, HCOFT provides a systematically improvable foundation for electronic-structure modeling and offers a promising pathway toward a versatile and unifying paradigm for accurate first-principles calculations.</p>\u0000 <p>This article is categorized under:\u0000\u0000 </p><ul>\u0000 \u0000 <li>Electronic Structure Theory &gt; Ab Initio Electronic Structure Methods</li>\u0000 \u0000 <li>Electronic Structure Theory &gt; Density Functional Theory</li>\u0000 </ul>\u0000 </div>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"16 2","pages":""},"PeriodicalIF":27.0,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147568345","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":"eMap 2.0: A Web-Based Platform for Identifying electron Transfer Pathways in Proteins and Protein Families","authors":"James R. Gayvert,&nbsp;Alyssa J. Kranc,&nbsp;Ruslan N. Tazhigulov,&nbsp;Ksenia B. Bravaya","doi":"10.1002/wcms.70071","DOIUrl":"10.1002/wcms.70071","url":null,"abstract":"<p>In this review we present eMap 2.0, a web-based application for predicting electron/hole transfer pathways in proteins and protein families based on their structures. The underlying model can be viewed as a coarse-grained version of the Pathways approach by Beratan and Onuchic [Beratan et al. J. Chem. Phys. 1987, 86, 4488]. Similar to the original framework, eMap employs graph-theory algorithms to search for the most efficient electron transfer pathways as shortest paths on a graph representation of the protein. In eMap, the nodes represent electron transfer active sites and only through-space tunneling is considered for each individual electron/hole hop. eMap 2.0 takes this model one step further by aiming at identifying shared electron transfer pathways in protein sets. From a graph theory standpoint, this is achieved using frequent subgraph mining (FSM) algorithms. Lastly, eMap 2.0 utilizes sequence and structural similarity measures to analyze and cluster the results. Here, we show how this robust method can be utilized to rapidly provide insights regarding conserved electron transfer pathways within protein families and to identify outliers, in which the conserved electron transfer pathway is blocked either by a mutation or conformational changes.</p><p>This article is categorized under:\u0000\u0000 </p>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"16 2","pages":""},"PeriodicalIF":27.0,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.70071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147565463","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":"Instantaneous Marcus Theory for Photoinduced Charge Transfer in Condensed Phase","authors":"Xiang Sun","doi":"10.1002/wcms.70069","DOIUrl":"10.1002/wcms.70069","url":null,"abstract":"<div>\u0000 \u0000 <p>Simulating photoinduced charge transfer (CT) in the condensed phase is essential for understanding solar energy conversion. Traditional Marcus theory is limited by its assumption of a thermally equilibrated initial state, which is often invalid for photoinduced processes, where vertical excitation creates a nonequilibrium nuclear state. The subsequent structural relaxation requires a time-dependent rate coefficient. This review focuses on Instantaneous Marcus Theory (IMT), an approach recently developed to capture these nonequilibrium effects. Derived as the classical limit of the nonequilibrium Fermi's golden rule (NE-FGR), IMT provides a practical, Marcus-like expression for the time-dependent rate based on the dynamical average and variance of the donor-acceptor energy gap. While the direct evaluation of IMT requires computationally expensive nonequilibrium molecular dynamics, the nonlinear-response (NLR) formulation reformulates the theory in terms of efficient equilibrium molecular dynamics simulations. This framework has been extended to multistate systems, allowing the simulation of complex reaction networks through a set of coupled Pauli's master equations. We highlight the application of these methods to the carotenoid-porphyrin-fullerene molecular triad, a prototypical organic photovoltaic system, dissolved in organic solvent. For this system, IMT correctly predicts a transient enhancement of the CT rate by over an order of magnitude, a nonequilibrium effect missed by Marcus theory. The population dynamics from multistate IMT are in excellent agreement with results from all-atom nonadiabatic semiclassical mapping dynamics and quantum NE-FGR calculations. This work establishes the multistate NLR-IMT method as a reliable and cost-effective tool for simulating photoinduced CT dynamics in realistic condensed-phase systems.</p>\u0000 <p>This article is categorized under:\u0000\u0000 </p><ul>\u0000 \u0000 <li>Theoretical and Physical Chemistry &gt; Reaction Dynamics and Kinetics</li>\u0000 \u0000 <li>Structure and Mechanism &gt; Reaction Mechanisms and Catalysis</li>\u0000 \u0000 <li>Software &gt; Simulation Methods</li>\u0000 </ul>\u0000 </div>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"16 2","pages":""},"PeriodicalIF":27.0,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564552","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":"Machine Learning Toolkits and Frameworks for Materials Design","authors":"B. Moses Abraham,&nbsp;Yury Gogotsi","doi":"10.1002/wcms.70067","DOIUrl":"10.1002/wcms.70067","url":null,"abstract":"<div>\u0000 \u0000 <p>The rapid evolution of machine learning (ML) has advanced materials discovery, providing tools to explore, predict, and design materials with tailored properties. Here we present an overview of emerging ML tools for data-driven materials innovation, including data curation, feature engineering, model development, interpretability, and inverse design. We highlight high-throughput material databases in providing large-scale, DFT-computed datasets, and discuss the importance of descriptor libraries that encode compositional and structural information into machine-readable inputs for model development. Advances in ML architectures, ranging from classical algorithms to graph neural networks, are discussed for their ability to capture complex structure–property relationships. Particular emphasis is given to inverse design frameworks using generative models and optimization strategies to enable property-targeted materials generation. We further explore interpretability and uncertainty quantification techniques that are important for bridging ML predictions with experimental validation. Automation platforms are described as tools for closed-loop, high-throughput discovery pipelines. We outline grand challenges, including data sparsity, model generalizability, and experimental integration. Finally, we summarize future directions that include foundation models pre-trained on broad, multimodal materials data; self-supervised learning strategies to reduce dependence on labeled datasets; ML workflows that embed thermodynamic and symmetry constraints to enhance interpretability; and fully autonomous laboratories that couple ML guidance with robotic synthesis and real-time feedback.</p>\u0000 <p>This article is categorized under:\u0000\u0000 </p><ul>\u0000 \u0000 <li>Structure and Mechanism &gt; Computational Materials Science</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":"16 2","pages":""},"PeriodicalIF":27.0,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147562889","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":"Toward Integrative Predictive Toxicology: Advanced Methods for Drug Toxicity and Safety Prediction","authors":"Eleonora Gianquinto,&nbsp;Matteo Bersani,&nbsp;Lucrezia Armando,&nbsp;Lara Davani,&nbsp;Clara Cena,&nbsp;Angela De Simone,&nbsp;Francesca Spyrakis","doi":"10.1002/wcms.70065","DOIUrl":"10.1002/wcms.70065","url":null,"abstract":"<p>The capability of anticipating and mitigating drug toxicity represents one of the most persistent challenges in drug development. Despite rigorous preclinical evaluation, nearly one third of drug candidates fail during the clinical phases due to safety issues, in particular hepatotoxicity and cardiotoxicity. Routine in vitro and in vivo toxicology tests, while essential to define safety margins, are frequently associated with high false-positive rates and poor translation to human outcomes. While investigative toxicology has improved mechanistic understanding of off-target interactions and toxicokinetics, the gap between preclinical findings and clinical safety still challenges efficient drug development. To overcome these drawbacks, the field is developing and applying more integrative strategies that combine computational modeling, machine learning, multi-omics technologies, and advanced in vitro systems. These approaches propose predictive pipelines likely able to identify chemical toxicology issues during early design stages and to characterize mechanisms of adverse events. In this review we provide a critical overview of emerging tools and strategies for drug toxicity prediction, evaluating their current impact, limitations, and translational potential to reduce safety-related attrition and support the development of safer therapeutics.</p><p>This article is categorized under:\u0000\u0000 </p>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"16 1","pages":""},"PeriodicalIF":27.0,"publicationDate":"2026-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://wires.onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.70065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147288313","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":"Correction to “Accelerating Covalent Drug Discovery: Recent Advances in Covalent Docking Tools”","authors":"","doi":"10.1002/wcms.70066","DOIUrl":"10.1002/wcms.70066","url":null,"abstract":"<p>\u0000 <span>S. Li</span>, <span>H. Du</span>, <span>H. Zhang</span>, <span>X. Zhang</span>, <span>Y. Ye</span>, <span>C. Shen</span>, <span>T. Hou</span>, and <span>P. Pan</span>, “ <span>Accelerating Covalent Drug Discovery: Recent Advances in Covalent Docking Tools</span>,” <i>Wiley Interdisciplinary Reviews. Computational Molecular Science</i> <span>15</span>, no. <span>6</span> (<span>2025</span>): e70062, https://doi.org/10.1002/wcms.70062.\u0000 </p><p>In the \"funding\" section, the text \"This work was supported by China Postdoctoral Science Foundation (Grant No. 2024M762886), National Natural Science Foundation of China (Grant Nos. 82204279, 82473843), Central Guidance for Local Science and Technology Development Funds Project (Grant No.2025ZY01022), and \"Pioneer\" and \"Leading Goose\" R&amp;D Program of Zhejiang (Grant No. 2025C01117).\" was incorrect.</p><p>This should have read as \"This work was financially supported by \"Pioneer\" and \"Leading Goose\" R&amp;D Program of Zhejiang (2025C01117), National Natural Science Foundation of China (82473843, 82204279), China Postdoctoral Science Foundation (2024M762886), and Central Guidance for Local Science and Technology Development Funds Project (2025ZY01022). We also thank the Information Technology Center and State Key Lab of CAD&amp;CG, Zhejiang University for the support of computing resources.\"</p><p>We apologize for this error.</p>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"16 1","pages":""},"PeriodicalIF":27.0,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://wires.onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.70066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146196933","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":"Breaking the Barriers of Molecular Dynamics With Deep-Learning: Opportunities, Pitfalls, and How to Navigate Them","authors":"Klara Bonneau,&nbsp;Aldo S. Pasos-Trejo,&nbsp;Michael Plainer,&nbsp;Luca Sagresti,&nbsp;Jacopo Venturin,&nbsp;Iryna Zaporozhets,&nbsp;Alessandro Caruso,&nbsp;Edoardo Rolando,&nbsp;Andrea Guljas,&nbsp;Leon Klein,&nbsp;Maximilian Schebek,&nbsp;Filippo Albani,&nbsp;Raquel López-Ríos de Castro,&nbsp;Zakariya El Machachi,&nbsp;Lorenzo Giambagli,&nbsp;Cecilia Clementi","doi":"10.1002/wcms.70064","DOIUrl":"10.1002/wcms.70064","url":null,"abstract":"<p>Molecular Dynamics (MD) has established itself as a pivotal computational tool across various scientific domains, including chemistry, biology, and materials science. Despite its widespread utility, MD faces inherent challenges, such as accuracy limitations, computational speed, and sampling efficiency. In recent years, machine learning, particularly deep learning, has seen significant advancements and is increasingly being integrated into MD processes. This review explores how deep learning can mitigate the issues associated with MD by addressing them from multiple angles. However, deep learning techniques introduce their own set of hurdles, including the need for extensive data, issues of interpretability, high computational costs, and concerns regarding transferability. Here, we discuss recent progress in the field of deep learning to overcome these obstacles. Ultimately, our goal is to demonstrate that, by leveraging the advancements made in both the MD and the machine learning community, deep learning has the potential to significantly enhance the capabilities of MD, paving the way to new scientific discovery.</p><p>This article is categorized under:\u0000\u0000 </p>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"16 1","pages":""},"PeriodicalIF":27.0,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://wires.onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.70064","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146091502","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":"From Collinear to Noncollinear Spin Density Functionals: The Multicollinear Approach","authors":"Tai Wang,&nbsp;Zhichen Pu,&nbsp;Hao Li,&nbsp;Qiming Sun,&nbsp;Yi Qin Gao,&nbsp;Yunlong Xiao","doi":"10.1002/wcms.70063","DOIUrl":"10.1002/wcms.70063","url":null,"abstract":"<div>\u0000 \u0000 <p>Most spin density functionals are collinear, assuming the spin magnetization has only one nonzero component. However, a fully defined functional should be noncollinear, treating all three components of the spin magnetization vector as variables. The multicollinear approach is introduced to bridge this gap by generalizing an arbitrary collinear functional to a noncollinear one. In contrast to the traditional scheme, which adopts the local projection of the spin magnetization vector field, the multicollinear method employs a global projection scheme. It offers several key advantages, including recovering the collinear limit, ensuring global spin rotational invariance, maintaining numerical stability, and providing nonzero local torque. Its broad applicability spans relativistic and nonrelativistic cases, molecular and periodic systems, ground and excited states, as well as static and dynamic simulations. Furthermore, for collinear systems, it provides capabilities that go beyond standard collinear functionals by establishing a rigorous framework for spin-flip TDDFT. This makes it a powerful tool for treating challenging problems such as double excitations, conical intersections, bond dissociation, and diradicals. Overall, the multicollinear approach provides a unified and versatile framework for quantum chemistry.</p>\u0000 <p>This article is categorized under:\u0000\u0000 </p><ul>\u0000 \u0000 <li>Electronic Structure Theory &gt; Density Functional Theory</li>\u0000 </ul>\u0000 </div>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"16 1","pages":""},"PeriodicalIF":27.0,"publicationDate":"2025-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145887695","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":"Accelerating Covalent Drug Discovery: Recent Advances in Covalent Docking Tools","authors":"Shi Li,&nbsp;Hongyan Du,&nbsp;Hui Zhang,&nbsp;Xujun Zhang,&nbsp;Yuanyi Ye,&nbsp;Chao Shen,&nbsp;Tingjun Hou,&nbsp;Peichen Pan","doi":"10.1002/wcms.70062","DOIUrl":"https://doi.org/10.1002/wcms.70062","url":null,"abstract":"<div>\u0000 \u0000 <p>Covalent inhibitors have garnered renewed attention in recent years, with their rational design becoming increasingly critical in drug discovery. Among the technologies facilitating the discovery of covalent inhibitors, covalent docking has emerged as a pivotal tool in various stages of drug development including virtual screening, lead optimization, and mechanistic studies. Since its inception as an extension of conventional docking methods in the early 2000s, covalent docking tools have undergone substantial advancements. This review provides a comprehensive overview of covalent docking algorithms, systematically categorizing their approaches according to covalent bond formation, which primarily include tethered docking, biased docking, and dynamic covalent docking approaches. A comparative analysis of current covalent docking tools is provided, alongside a critical discussion of remaining challenges. Special emphasis is placed on the growing impact of artificial intelligence (AI) in shaping novel methodologies and expanding the capabilities of covalent docking. Finally, we discuss prospects for advancing covalent docking methodologies and their applications in drug discovery.</p>\u0000 <p>This article is categorized under:\u0000\u0000 </p><ul>\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":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145845968","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}