Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

The resolution-of-the-identity (RI) or density fitting (DF) approximation for the electron repulsion integrals (ERIs) has become a standard component of accelerated and reduced-scaling implementations of first-principles Gaussian-type orbital electronic-structure methods. The Cholesky decomposition (CD) of the ERIs has also become increasingly deployed across quantum chemistry packages in the last decade, even though its early applications were mostly limited to high-accuracy methods such as coupled-cluster theory and multiconfigurational approaches. Starting with a summary of the basic theory underpinning both the CD and RI/DF approximations, thus underlining the extremely close relation of the CD and RI/DF techniques, we provide a brief and largely chronological review of the evolution of the CD approach from its birth in 1977 to its current state. In addition to being a purely numerical procedure for handling ERIs, thus providing robust and computationally efficient approximations to the exact ERIs that have been found increasingly useful on modern computer platforms, CD also offers highly accurate approaches for generating auxiliary basis sets for the RI/DF approximation on the fly due to the deep mathematical connection between the two approaches. In this review, we aim to provide a concise reference of the main techniques employed in various CD approaches in electronic structure theory, to exemplify the connection between the CD and RI/DF approaches, and to clarify the state of the art to guide new implementations of CD approaches across electronic structure programs.

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Retrosynthesis is the cornerstone of organic chemistry, providing chemists in material and drug manufacturing access to poorly available and brand-new molecules. Conventional rule-based or expert-based computer-aided synthesis has obvious limitations, such as high labor costs and limited search space. In recent years, dramatic breakthroughs driven by deep learning have revolutionized retrosynthesis. Here we aim to present a comprehensive review of recent advances in AI-based retrosynthesis. For single-step and multi-step retrosynthesis both, we first introduce their goal and provide a thorough taxonomy of existing methods. Afterwards, we analyze these methods in terms of their mechanism and performance, and introduce popular evaluation metrics for them, in which we also provide a detailed comparison among representative methods on several public datasets. In the next part, we introduce popular databases and established platforms for retrosynthesis. Finally, this review concludes with a discussion about promising research directions in this field.

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All processes involving molecular systems entail a balance between associated enthalpic and entropic changes. Molecular dynamics simulations of the end-points of a process provide in a straightforward way the enthalpy as an ensemble average. Obtaining absolute entropies is still an open problem and most commonly pathway methods are used to obtain free energy changes and thereafter entropy changes. The kth nearest neighbor (kNN) method has been first proposed as a general method for entropy estimation in the mathematical community 20 years ago. Later, it has been applied to compute conformational, positional–orientational, and hydration entropies of molecules. Programs to compute entropies from molecular ensembles, for example, from molecular dynamics (MD) trajectories, based on the kNN method, are currently available. The kNN method has distinct advantages over traditional methods, namely that it is possible to address high-dimensional spaces, impossible to treat without loss of resolution or drastic approximations with, for example, histogram-based methods. Application of the method requires understanding the features of: the kth nearest neighbor method for entropy estimation; the variables relevant to biomolecular and in general molecular processes; the metrics associated with such variables; the practical implementation of the method, including requirements and limitations intrinsic to the method; and the applications for conformational, position/orientation and solvation entropy. Coupling the method with general approximations for the multivariable entropy based on mutual information, it is possible to address high dimensional problems like those involving the conformation of proteins, nucleic acids, binding of molecules and hydration.

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Polaritonic chemistry is an interdisciplinary emerging field that presents several challenges and opportunities in chemistry, physics, and engineering. A systematic review of polaritonic response theory is presented, following a chemical perspective based on molecular response theory. We provide the reader with a general strategy for developing response theory for ab initio cavity quantum electrodynamics (QED) methods and critically emphasize details that still need clarification and require cooperation between the physical and chemistry communities. We show that several well-established results can be applied to strong coupling light-matter systems, leading to novel perspectives on the computation of matter and photonic properties. The application of the Pauli–Fierz Hamiltonian to polaritons is discussed, focusing on the effects of describing operators in different mathematical representations. We thoroughly examine the most common approximations employed in ab initio QED, such as the dipole approximation. We introduce the polaritonic response equations for the recently developed ab initio QED Hartree–Fock and QED coupled cluster methods. The discussion focuses on the similarities and differences from standard quantum chemistry methods, providing practical equations for computing the polaritonic properties.

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Conformational searches and ML-driven geometry predictions (e.g., AlphaFold) work in the space of molecule's degrees of freedom. When dealing with cycles, cyclicity constraints impose complex interdependence between them, so that arbitrary changes of cyclic dihedral angles lead to heavy distortions of some bond lengths and valence angles of the ring. This renders navigation through conformational space of cyclic molecules to be very challenging. Inverse kinematics is a theory that provides a mathematically strict solution to this problem. It allows one to identify degrees of freedom for any polycyclic molecule, that is, its dihedral angles that can be set independently from each other. Then for any values of degrees of freedom, inverse kinematics can reconstruct the remaining dihedrals so that all rings are closed with given bond lengths and valence angles. This approach offers a handy and efficient way for constructing and navigating conformational space of any molecule using either naïve Monte-Carlo sampling or sophisticated machine learning models. Inverse kinematics can considerably narrow the conformational space of a polycyclic molecule to include only cyclicity-preserving regions. Thus, it can be viewed as a physical constraint on the model, making the latter obey the laws of kinematics, which govern the rings conformations. We believe that inverse kinematics will be universally used in the ever-growing field of geometry prediction of complex interlinked molecules.

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There is a variety of experimental and computational techniques available to explore protein dynamics, each presenting advantages and limitations. One promising experimental technique that is driving the development of computational methods is cryo-electron microscopy (cryo-EM). Cryo-EM provides molecular-level structural data and first estimates of conformational landscape from single particle analysis but cannot track real-time protein dynamics and may contain uncertainties in atomic positions especially at highly dynamic regions. Molecular simulations offer atomic-level insights into protein dynamics; however, their computing time requirements limit the conformational sampling accuracy, and it is often hard, to assess by full-atomic simulations the cooperative movements of biological interest for large assemblies such as those resolved by cryo-EM. Coarse-grained (CG) simulations permit us to explore such systems, but at the costs of lower resolution and potentially incomplete sampling of conformational space. On the other hand, analytical methods may circumvent sampling limitations. In particular, elastic network models-based normal mode analyses (ENM-NMA) provide unique solutions for the complete mode spectra near equilibrium states, even for systems of megadaltons, and may thus deliver information on mechanisms of motions relevant to biological function. Yet, they lack atomic resolution as well as temporal information for non-equilibrium systems. Given the complementary nature of these methods, the integration of molecular simulations and ENM-NMA into hybrid methodologies has gained traction. This review presents the current state-of-the-art in structure-based computations and how they are helping us gain a deeper understanding of biological mechanisms, with emphasis on the development of hybrid methods accompanying the advances in cryo-EM.

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The cover image is based on the Software Focus AQME: Automated quantum mechanical environments for researchers and educators by Juan V. Alegre-Requena et al., https://doi.org/10.1002/wcms.1663.

The numerical atomic orbital (NAO) basis sets offer a computationally efficient option for electronic structure calculations, as they require fewer basis functions compared with other types of basis sets. Moreover, their strict localization allows for easy combination with current linear scaling methods, enabling efficient calculation of large physical systems. In recent years, NAO bases have become increasingly popular in modern electronic structure codes. This article provides a review of the ab initio electronic structure calculations using NAO bases. We begin by introducing basic formalisms of the NAO-based electronic structure method, including NAO base set generation, self-consistent calculations, force, and stress calculations. We will then discuss some recent advances in the methods based on the NAO bases, such as real-time dependent density functional theory (rt-TDDFT), efficient implementation of hybrid functionals, and other advanced electronic structure methods. Finally, we introduce the ab initio tight-binding model, which can be generated directly after the self-consistent calculations. The model allows for efficient calculation of electronic structures, and the associated topological, and optical properties of the systems.

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Titanium dioxide (TiO₂) is one of the most technologically promising oxides with a broad range of catalytic and photocatalytic activities. Theoretical modeling, especially density functional theory calculations, has been extensively carried out to understand the geometric structure, electronic structure, reactivity, and reaction mechanisms of TiO₂ systems, as well as to develop new catalysts with improved performances. This review summarizes the recent theoretical progress on the well-defined surfaces of TiO₂ crystalline, and focuses on the structures, adsorptions, and reactions on the surface and at the interface. The theoretical methods and models, surface defects, surface doping, water splitting and H₂ evolution, methanol conversion, CO₂ reduction and CO oxidation, SO_x and NO_x degradation, CH₄ conversion, organic pollutant degradation, CH bond activation and CC bond formation, dye sensitization, as well as the applications of TiO₂ in some other fields, have been discussed in detail.

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Intrinsically disordered proteins (IDPs) are proteins that perform important biological functions without well-defined structures under physiological conditions. IDPs can form fuzzy complexes with other molecules, participate in the formation of membraneless organelles, and function as hubs in protein–protein interaction networks. The malfunction of IDPs causes major human diseases. However, drug design targeting IDPs remains challenging due to their highly dynamic structures and fuzzy interactions. Turning IDPs into druggable targets provides a great opportunity to extend the druggable target-space for novel drug discovery. Integrative structural biology approaches that combine information derived from computational simulations, artificial intelligence/data-driven analysis and experimental studies have been used to uncover the dynamic structures and interactions of IDPs. An increasing number of ligands that directly bind IDPs have been found either by target-based experimental and computational screening or phenotypic screening. Along with the understanding of IDP binding with its partners, structure-based drug design strategies, especially conformational ensemble-based computational ligand screening and computer-aided ligand optimization algorithms, have greatly accelerated the development of IDP ligands. It is inspiring that several IDP-targeting small-molecule and peptide drugs have advanced into clinical trials. However, new computational methods need to be further developed for efficiently discovering and optimizing specific and potent ligands for the vast number of IDPs. Along with the understanding of their dynamic structures and interactions, IDPs are expected to become valuable treasure of drug targets.

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