Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献_第9页

Atomistic neural network representations for chemical dynamics simulations of molecular, condensed phase, and interfacial systems: Efficiency, representability, and generalization 分子、凝聚相和界面系统化学动力学模拟的原子神经网络表征:效率、可表征性和泛化

IF 11.4 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2022-11-16 DOI: 10.1002/wcms.1645

Yaolong Zhang, Qidong Lin, Bin Jiang

{"title":"Atomistic neural network representations for chemical dynamics simulations of molecular, condensed phase, and interfacial systems: Efficiency, representability, and generalization","authors":"Yaolong Zhang, Qidong Lin, Bin Jiang","doi":"10.1002/wcms.1645","DOIUrl":"https://doi.org/10.1002/wcms.1645","url":null,"abstract":"Machine learning techniques have been widely applied in many fields of chemistry, physics, biology, and materials science. One of the most fruitful applications is machine learning of the complicated multidimensional function of potential energy or related electronic properties from discrete quantum chemical data. In particular, substantial efforts have been dedicated to developing various atomistic neural network (AtNN) representations, which refer to a family of methods expressing the targeted physical quantity as a sum of atomic components represented by atomic NNs. This class of approaches not only fully preserves the physical symmetry of the system but also scales linearly with respect to the size of a system, enabling accurate and efficient chemical dynamics and spectroscopic simulations in complicated systems and even a number of variably sized systems across the phases. In this review, we discuss different strategies in developing highly efficient and representable AtNN potentials, and in generalizing these scalar AtNN models to learn vectorial and tensorial quantities with the correct rotational equivariance. We also review active learning algorithms to generate practical AtNN models and present selected examples of AtNN applications in gas-surface systems to demonstrate their capabilities of accurately representing both molecular systems and condensed phase systems. We conclude this review by pointing out remaining challenges for the further development of more reliable, transferable, and scalable AtNN representations in more application scenarios.This article is categorized under:\u0000 ","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"13 3","pages":""},"PeriodicalIF":11.4,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"5663359","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}

引用次数: 10

Computational protein design with data-driven approaches: Recent developments and perspectives 用数据驱动的方法计算蛋白质设计:最近的发展和观点

IF 11.4 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2022-11-15 DOI: 10.1002/wcms.1646

Haiyan Liu, Quan Chen

引用次数: 3

Establishing the catalytic and regulatory mechanism of RNA-based machineries 建立基于rna的机制的催化调控机制

IF 11.4 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2022-10-21 DOI: 10.1002/wcms.1643

Jure Bori?ek, Jana Aupi?, Alessandra Magistrato

{"title":"Establishing the catalytic and regulatory mechanism of RNA-based machineries","authors":"Jure Bori?ek, Jana Aupi?, Alessandra Magistrato","doi":"10.1002/wcms.1643","DOIUrl":"https://doi.org/10.1002/wcms.1643","url":null,"abstract":"Ribonucleoprotein (RNP)-machineries are comprised of intricate networks of long noncoding RNAs and proteins that allow them to actively participate in transcription, RNA processing, and translation. RNP-machineries thus play vital roles in gene expression and regulation. Recent advances in cryo-EM techniques provided a wealth of near-atomic-level resolution structures setting the basis for understanding how these fascinating multiscale complexes exert their diverse roles. However, these structures represent only isolated snapshots of the plastic and highly dynamic RNP-machineries and are thus insufficient to comprehensively assess their multifaceted mechanisms. In this review, we discuss the role and merit of all-atom simulations in disentangling the mechanism of eukaryotic RNA-based machineries responsible for RNA processing. We showcase how all-atom simulations can capture their large-scale functional movements, trace the signaling pathways that are at the root of their massive conformational remodeling, explain recognition mechanisms of specific RNA sequences, and, lastly, unravel the chemical mechanisms underlying the formation of functional RNA strands. Finally, we review the methodological pitfalls and outline future challenges in modeling key functional aspects of these large molecular engines with all-atom simulations. In addition to providing insights into the most basic processes that govern all forms of life, in-depth mechanistic comprehension of RNP-machineries offers a foundation for developing innovative therapeutic strategies against the variety of human diseases linked to deregulated RNA metabolism.This article is categorized under:\u0000 ","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"13 3","pages":""},"PeriodicalIF":11.4,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"5796578","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}

引用次数: 5

Recent advances in computational studies on voltage-gated sodium channels: Drug design and mechanism studies 电压门控钠通道的计算研究进展:药物设计和机制研究

IF 11.4 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2022-10-20 DOI: 10.1002/wcms.1641

Gaoang Wang, Lei Xu, Haiyi Chen, Yifei Liu, Peichen Pan, Tingjun Hou

引用次数: 1

Chemical transformations and transport phenomena at interfaces 界面上的化学转变和输运现象

IF 11.4 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2022-10-18 DOI: 10.1002/wcms.1639

Hongxia Hao, Luis Ruiz Pestana, Jin Qian, Meili Liu, Qiang Xu, Teresa Head-Gordon

引用次数: 2

Small molecule superposition: A comprehensive overview on pose scoring of the latest methods 小分子叠加:姿态评分最新方法的综合概述

IF 11.4 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2022-10-11 DOI: 10.1002/wcms.1640

Sophia M. N. H?nig, Christian Lemmen, Matthias Rarey

引用次数: 3

Mechanistic aspects of thiol additions to Michael acceptors: Insights from computations 巯基添加到迈克尔受体的机理:来自计算的见解

IF 11.4 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2022-09-21 DOI: 10.1002/wcms.1636

Ras Baizureen Roseli, Angus B. Keto, Elizabeth H. Krenske

引用次数: 3

Efficient and enhanced sampling of drug-like chemical space for virtual screening and molecular design using modern machine learning methods 使用现代机器学习方法进行虚拟筛选和分子设计的药物样化学空间的高效和增强采样

IF 11.4 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2022-09-16 DOI: 10.1002/wcms.1637

Manan Goel, Rishal Aggarwal, Bhuvanesh Sridharan, Pradeep Kumar Pal, U. Deva Priyakumar

{"title":"Efficient and enhanced sampling of drug-like chemical space for virtual screening and molecular design using modern machine learning methods","authors":"Manan Goel, Rishal Aggarwal, Bhuvanesh Sridharan, Pradeep Kumar Pal, U. Deva Priyakumar","doi":"10.1002/wcms.1637","DOIUrl":"https://doi.org/10.1002/wcms.1637","url":null,"abstract":"Drug design involves the process of identifying and designing novel molecules that have desirable properties and bind well to a given target receptor. Typically, such molecules are identified by screening large chemical libraries for desirable physicochemical properties and binding strength with the target protein. This traditional approach, however, has severe limitations as exhaustively screening every molecule in known chemical libraries is computationally infeasible. Furthermore, currently available molecular libraries are only a minuscule part of the entire set of possible drug-like molecular structures (drug-like chemical space). In this review, we discuss how the former limitation is addressed by modeling virtual screening as a search space problem and how these endeavors utilize machine learning to reduce the number of required computational experiments to identify top candidates. We follow that up by discussing generative methods that attempt to approximate the entire drug-like chemical space providing us a path to explore beyond the known drug-like chemical space. We place special emphasis on generative models that learn the marginal distributions conditioned on specific properties or receptor structures for efficient sampling of molecules. Through this review, we aim to highlight modern machine learning based methods that try to efficiently enhance our sampling capability beyond conventional screening methods which, in turn, would benefit drug design significantly. Therefore, we also encourage further methods of development that work on such important aspects of drug design.This article is categorized under:\u0000 ","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"13 2","pages":""},"PeriodicalIF":11.4,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"5894133","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}

引用次数: 7

Synthesis of two-dimensional materials: How computational studies can help? 二维材料的合成:计算研究如何提供帮助?

IF 11.4 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2022-09-08 DOI: 10.1002/wcms.1635

Yanqing Guo, Yishan Hu, Qinghong Yuan

{"title":"Synthesis of two-dimensional materials: How computational studies can help?","authors":"Yanqing Guo, Yishan Hu, Qinghong Yuan","doi":"10.1002/wcms.1635","DOIUrl":"https://doi.org/10.1002/wcms.1635","url":null,"abstract":"The scalable preparation of high-quality and low-cost two-dimensional (2D) materials is critical to achieving their potential applications in various fields. Chemical vapor deposition (CVD) method is considered the most promising method for producing ultrathin 2D materials and has continued to develop in recent years. First-principles calculations have provided important theoretical guidance for the CVD synthesis of 2D materials, and have played an increasingly important role in the field of material synthesis in recent years. In this review, we present recent advances in the growth mechanism of 2D materials, focusing on the theoretical research progress of four typical 2D materials: graphene, hexagonal boron nitride (hBN), transition metal dichalcogenide (TMDC), and phosphorene. Several aspects of the growth process are discussed in detail, including the decomposition of precursors, nucleation, growth kinetics, domain shape, and epitaxial and alignment of 2D crystals. Based on the understanding of these atomic-scale growth processes, strategies toward the wafer-scale growth of continuous and homogeneous 2D thin films are proposed and confirmed by experiments. In the final section, we summarize future challenges and opportunities in the computational studies of the growth mechanism of 2D materials.This article is categorized under:\u0000 ","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"13 2","pages":""},"PeriodicalIF":11.4,"publicationDate":"2022-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"6139654","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}

引用次数: 1

Cover Image, Volume 12, Issue 5 封面图片，第12卷，第5期

IF 11.4 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2022-09-08 DOI: 10.1002/wcms.1638

Philippe Schwaller, Alain C. Vaucher, Ruben Laplaza, Charlotte Bunne, Andreas Krause, Clemence Corminboeuf, Teodoro Laino

引用次数: 0