{"title":"Computational insights into the rational design of organic electrode materials for metal ion batteries","authors":"Xinyue Zhu, Youchao Yang, Xipeng Shu, Tianze Xu, Yu Jing","doi":"10.1002/wcms.1660","DOIUrl":"https://doi.org/10.1002/wcms.1660","url":null,"abstract":"<p>Metal ion batteries (MIBs), represented by lithium ion batteries are important energy storage devices for storing renewable energy. Advanced development of MIBs depends on the exploration of efficient and sustainable electrode materials. Organic electrode materials (OEMs) with redox-active moieties are low-cost and eco-friendly alternatives to conventional inorganic electrode materials for MIBs. Computational simulation plays an important role in understanding the energy storage mechanism of different active functional groups and boosting the discovery of new OEMs for high-efficient MIBs. Here, we will review recent progress of OEMs and comprehensively survey factors that determine their electrochemical properties. Dependable computational methods to guide the design of OEMs are comprehensively discussed and machine learning is highlighted as an emerging method to reveal the underlying structure–performance relationship and facilitate screening of OEMs with high-efficiency. Finally, we summarize the available molecular design strategies to effectively improve the redox activity and stability of OEMs, and discuss challenges and opportunities of theoretical calculations of OEMs for MIBs.</p><p>This article is categorized under:\u0000 </p>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"13 5","pages":""},"PeriodicalIF":11.4,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41081620","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":"A promising intersection of excited-state-specific methods from quantum chemistry and quantum Monte Carlo","authors":"Leon Otis, Eric Neuscamman","doi":"10.1002/wcms.1659","DOIUrl":"https://doi.org/10.1002/wcms.1659","url":null,"abstract":"<p>We present a discussion of recent progress in excited-state-specific quantum chemistry and quantum Monte Carlo alongside a demonstration of how a combination of methods from these two fields can offer reliably accurate excited state predictions across singly excited, doubly excited, and charge transfer states. Both of these fields have seen important advances supporting excited state simulation in recent years, including the introduction of more effective excited-state-specific optimization methods, improved handling of complicated wave function forms, and ways of explicitly balancing the quality of wave functions for ground and excited states. To emphasize the promise that exists at this intersection, we provide demonstrations using a combination of excited-state-specific complete active space self-consistent field theory, selected configuration interaction, and state-specific variance minimization. These demonstrations show that combining excited-state-specific quantum chemistry and variational Monte Carlo can be more reliably accurate than either equation of motion coupled cluster theory or multi-reference perturbation theory, and that it can offer new clarity in cases where existing high-level methods do not agree.</p><p>This article is categorized under:\u0000 </p>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"13 5","pages":""},"PeriodicalIF":11.4,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41081683","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":"Quantitative analysis of high-throughput biological data","authors":"Hsueh-Fen Juan, Hsuan-Cheng Huang","doi":"10.1002/wcms.1658","DOIUrl":"https://doi.org/10.1002/wcms.1658","url":null,"abstract":"<p>The study of multiple “omes,” such as the genome, transcriptome, proteome, and metabolome has become widespread in biomedical research. High-throughput techniques enable the rapid generation of high-dimensional multiomics data. This multiomics approach provides a more complete perspective to study biological systems compared with traditional methods. However, the quantitative analysis and integration of distinct types of high-dimensional omics data remain a challenge. Here, we provide an up-to-date and comprehensive review of the methods used for omics data quantification and integration. We first review the quantitative analysis of not only bulk but also single-cell transcriptomics data, as well as proteomics data. Current methods for reducing batch effects and integrating heterogeneous high-dimensional data are then introduced. Network analysis on large-scale biomedical data can capture the global properties of drugs, targets, and disease relationships, thus enabling a better understanding of biological systems. Current trends in the applications and methods used to extend quantitative omics data analysis to biological networks are also discussed.</p><p>This article is categorized under:\u0000 </p>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"13 4","pages":""},"PeriodicalIF":11.4,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"6020109","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":"Universal QM/MM approaches for general nanoscale applications","authors":"Katja-Sophia Csizi, Markus Reiher","doi":"10.1002/wcms.1656","DOIUrl":"https://doi.org/10.1002/wcms.1656","url":null,"abstract":"<p>Quantum mechanics/molecular mechanics (QM/MM) hybrid models allow one to address chemical phenomena in complex molecular environments. Whereas this modeling approach can cope with a large system size at moderate computational costs, the models are often tedious to construct and require manual preprocessing and expertise. As a result, transferability to new application areas can be limited and the many parameters are not easy to adjust to reference data that are typically scarce. Therefore, it is desirable to devise automated procedures of controllable accuracy, which enables such modeling in a standardized and black-box-type manner. Although diverse best-practice protocols have been set up for the construction of individual components of a QM/MM model (e.g., the MM potential, the type of embedding, the choice of the QM region), automated procedures that reconcile all steps of the QM/MM model construction are still rare. Here, we review the state of the art of QM/MM modeling with a focus on automation. We elaborate on MM model parametrization, on atom-economical physically-motivated QM region selection, and on embedding schemes that incorporate mutual polarization as critical components of the QM/MM model. In view of the broad scope of the field, we mostly restrict the discussion to methodologies that build <i>de novo</i> models based on first-principles data, on uncertainty quantification, and on error mitigation with a high potential for automation. Ultimately, it is desirable to be able to set up reliable QM/MM models in a fast and efficient automated way without being constrained by specific chemical or technical limitations.</p><p>This article is categorized under:\u0000 </p>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"13 4","pages":""},"PeriodicalIF":11.4,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.1656","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"6009884","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}
Siri C. van Keulen, Juliette Martin, Francesco Colizzi, Elisa Frezza, Daniel Trpevski, Nuria Cirauqui Diaz, Pietro Vidossich, Ursula Rothlisberger, Jeanette Hellgren Kotaleski, Rebecca C. Wade, Paolo Carloni
{"title":"Cover Image, Volume 13, Issue 1","authors":"Siri C. van Keulen, Juliette Martin, Francesco Colizzi, Elisa Frezza, Daniel Trpevski, Nuria Cirauqui Diaz, Pietro Vidossich, Ursula Rothlisberger, Jeanette Hellgren Kotaleski, Rebecca C. Wade, Paolo Carloni","doi":"10.1002/wcms.1657","DOIUrl":"https://doi.org/10.1002/wcms.1657","url":null,"abstract":"<p>The cover image is based on the Focus Article <i>Multiscale molecular simulations to investigate adenylyl cyclase-based signaling in the brain</i> by Siri C. van Keulen et al., https://doi.org/10.1002/wcms.1623. Image Credit: F. Colizzi\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"13 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.1657","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"5731079","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":"Atomistic simulations of pristine and nanoparticle reinforced hydrogels: A review","authors":"Raju Kumar, Avinash Parashar","doi":"10.1002/wcms.1655","DOIUrl":"https://doi.org/10.1002/wcms.1655","url":null,"abstract":"<p>Hydrogel is a three-dimensional cross-linked hydrophilic network that can imbibe a large amount of water inside its structure (up to 99% of its dry weight). Due to their unique characteristics of biocompatibility and flexibility, it has found applications in diversified fields, including tissue engineering, drug delivery, biosensors, and agriculture. Even though hydrogels are widely used in the biomedical field, their lower mechanical strength still limits their application to its full potential. Hydrogels can be reinforced with organic, inorganic, and metal-based nanofillers to improve their mechanical strength. Due to improved computational power, computational-based techniques are emerging as a leading characterization technique for nanocomposites and hydrogels. In nanomaterials, atomistic description governs the mechanical strength and thermal behavior that realized atomistic level simulations as an appropriate approach to capture the deformation governing mechanism. Among atomistic simulations, the molecular dynamics (MD)-based approach is emerging as a prospective technique for simulating neat and nanocomposite-based hydrogels' mechanical and thermal behavior. The success and accuracy of MD simulation entirely depend on the force field. This review article will compile the force field employed by the research community to capture the atomistic interactions in different nanocomposite-based hydrogels. This article will comprehensively review the progress made in the atomistic approach to study neat and nanocomposite-based hydrogels' properties. The authors have enlightened the challenges and limitations associated with the atomistic modeling of hydrogels.</p><p>This article is categorized under:\u0000 </p>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"13 4","pages":""},"PeriodicalIF":11.4,"publicationDate":"2023-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"5917459","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":"Graph neural networks for conditional de novo drug design","authors":"Carlo Abate, Sergio Decherchi, Andrea Cavalli","doi":"10.1002/wcms.1651","DOIUrl":"https://doi.org/10.1002/wcms.1651","url":null,"abstract":"<p>Drug design is costly in terms of resources and time. Generative deep learning techniques are using increasing amounts of biochemical data and computing power to pave the way for a new generation of tools and methods for drug discovery and optimization. Although early methods used SMILES strings, more recent approaches use molecular graphs to naturally represent chemical entities. Graph neural networks (GNNs) are learning models that can natively process graphs. The use of GNNs in drug discovery is growing exponentially. GNNs for drug design are often coupled with conditioning techniques to steer the generation process towards desired chemical and biological properties. These conditioned graph-based generative models and frameworks hold promise for the routine application of GNNs in drug discovery.</p><p>This article is categorized under:\u0000 </p>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"13 4","pages":""},"PeriodicalIF":11.4,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"6195716","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}
Gaomou Xu, Cheng Cai, Wanghui Zhao, Yonghua Liu, Tao Wang
{"title":"Rational design of catalysts with earth-abundant elements","authors":"Gaomou Xu, Cheng Cai, Wanghui Zhao, Yonghua Liu, Tao Wang","doi":"10.1002/wcms.1654","DOIUrl":"https://doi.org/10.1002/wcms.1654","url":null,"abstract":"<p>Catalysis has played a crucial role in energy sustainability, environment control, and chemical production, while the design of high-performance catalysts is a key scientific question. In nature, biological organisms carry out catalysis with earth-abundant metals, whereas modern industrial processes rely heavily on precious metals. This points out the necessity of designing state-of-the-art catalysts with earth-abundant elements to maintain sustainable catalysis. In this review, we will start with the fact that nature uses earth-abundant metals to feed the planet, followed by a few successful examples of catalyst design for water oxidation. Then, we will systematically introduce the practical methods in computational catalyst design and their applications in the rational modification of EAM catalysts for various reactions. In addition, the roles of high-throughput computations and artificial intelligence in this framework are summarized and discussed. We will also discuss the potential limitations of the framework and the strategies to overcome these challenges. Finally, we emphasize the importance of the synergistic efforts between theory and experiments in rational catalyst design with earth-abundant elements.</p><p>This article is categorized under:\u0000 </p>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"13 4","pages":""},"PeriodicalIF":11.4,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"6108357","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}
Carles Perez-Lopez, Alexis Molina, Estrella Lozoya, Victor Segarra, Marti Municoy, Victor Guallar
{"title":"Combining machine-learning and molecular-modeling methods for drug-target affinity predictions","authors":"Carles Perez-Lopez, Alexis Molina, Estrella Lozoya, Victor Segarra, Marti Municoy, Victor Guallar","doi":"10.1002/wcms.1653","DOIUrl":"https://doi.org/10.1002/wcms.1653","url":null,"abstract":"<p>Machine learning (ML) techniques offer a novel and exciting approach in the drug discovery field. One might even argue that their current expansion may push traditional MM modeling techniques to a secondary role in modeling methods. In this review article, we advocate that a combination of both techniques could be the most efficient implementation in the coming years. Focusing on drug-target affinity predictions, we first review pure ML approaches. Then, we introduced recent developments in mixing ML and MM methods in a single combined manner. Finally, we show the detailed implementation of a real industrial prospective study where nanomolar hits, on a kinase target, were obtained by combination of state of the art Monte Carlo MM simulations (PELE) with a ML ranking function.</p><p>This article is categorized under:\u0000 </p>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"13 4","pages":""},"PeriodicalIF":11.4,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"6065019","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":"Perspective: Simultaneous treatment of relativity, correlation, and QED","authors":"Wenjian Liu","doi":"10.1002/wcms.1652","DOIUrl":"https://doi.org/10.1002/wcms.1652","url":null,"abstract":"<p>Electronic structure calculations of many-electron systems should in principle treat relativistic, correlation, and quantum electrodynamics (QED) effects simultaneously to a high precision, so as to match experimental measurements as close as possible. While both relativistic and QED effects can readily be built into the many-electron Hamiltonian, electron correlation is more difficult to describe due to the exponential growth of the number of parameters in the wave function. Compared with the spin-free case, spin–orbit interaction results in the loss of spin symmetry and concomitant complex algebra, thereby rendering the treatment of electron correlation even more difficult. Possible solutions to these issues are highlighted here.</p><p>This article is categorized under:\u0000 </p>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"13 4","pages":""},"PeriodicalIF":11.4,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"5744339","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}