Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

Metal nanomaterials are of great importance in the field of heterogeneous catalysis. In general, the catalytic performances of metal nanomaterials are determined by the structures. However, far from being static, dynamic reconstruction of metal nanomaterials constantly occurs in reactive environments, resulting in different catalytic activities. This review summarizes the latest progress of theoretical understanding of the driving forces for the structural changes. In the first part, some typical ex situ and in situ experimental observations of catalysts in reactive environments are briefly introduced, including the changes of shape, size, and alloy composition of metal or bimetallic nanomaterials. Next, we review the state-of-the-art advancement of the theoretical calculations and simulation methods to understand these experimental observations, and categorize them according to the different driving forces, for example, the oxidation and reduction effects, adsorption-induced reconstruction. Moreover, this review provides many examples for the quantitative agreement between theoretical modeling and experimental observations, which indicates the potential applications for the rational design of high-performance metal nanocatalysts in real reactions.

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Hindered rotations are common in nature and can greatly affect thermodynamic properties. Typically, the standard rigid-rotor harmonic-oscillator approximation is used to compute thermodynamic properties; however, it often leads to serious errors, particularly for molecules with hindered rotations. Hence, to reach accurate thermodynamic predictions for such cases, the hindered rotor approximation must be applied. Different methods to compute thermodynamic properties for molecules with hindered rotations are available. Herein, we review the theoretical basis of different methods, their accuracy, and applicability. We also present the different algorithms to identify hindered rotors and obtain the input parameters for the hindered rotor model, and the software available to compute thermodynamic properties under this scheme.

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Vibrational frequency calculations performed under the harmonic approximation are widespread across chemistry. However, it is well-known that the calculated harmonic frequencies tend to systematically overestimate experimental fundamental frequencies; a limitation commonly overcome with multiplicative scaling factors. In practice, multiplicative scaling factors are derived for each individual model chemistry choice (i.e., a level of theory and basis set pair), where performance is judged by, for example, the root-mean square error (RMSE) between the predicted scaled and experimental frequencies. However, despite the overwhelming number of scaling factors reported in the literature and model chemistry approximations available, there is little guidance for users on appropriate model chemistry choices for harmonic frequency calculations. Here, we compile and analyze the data for 1495 scaling factors calculated using 141 levels of theory and 109 basis sets. Our meta-analysis of this data shows that scaling factors and RMSE approach convergence with only hybrid functionals and double-zeta basis sets, with anharmonicity error already dominating model chemistry errors. Noting inconsistent data and the lack of independent testing, we can nevertheless conclude that a minimum error of 25 cm⁻¹—arising from insufficiently accurate treatment of anharmonicity—is persistent regardless of the model chemistry choice. Based on the data we compiled and cautioning the need for a future systematic benchmarking study, we recommend ωB97X-D/def2-TZVP for most applications and B2PLYP/def2-TZVPD for superior intensity predictions. With a smaller benchmark set, direct comparison prefers ωB97X-D/6-31G* to B3LYP/6-31G*.

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Allostery is a universal, biological phenomenon in which orthosteric sites are fine-tuned by topologically distal allosteric sites triggered by perturbations, such as ligand binding, residue mutations, or post-translational modifications. Allosteric regulation is implicated in a variety of physiological and pathological conditions and is thus emerging as a novel avenue for drug discovery. Allosteric drugs have traditionally been discovered by serendipity through large-scale experimental screening. Recently, we have witnessed significant progress in biophysics, particularly in structural bioinformatics, which has facilitated the in-depth characterization of allosteric effects and the accurate detection of allosteric residues and exosites. These advances improve our understanding of allosterism and promote allosteric drug discovery, thereby revolutionizing the shift from the traditional serendipitous route used to discover allosteric drugs to the updated path centered on rational structure-based design. In this review, recent advances in computational methods applied to allosteric drug discovery are summarized. We comprehensively review these achievements along various levels of allosteric events, from the construction of allosteric databases to the identification and analysis of allosteric residues, signals, sites, and modulators. We expect to increase the awareness of the discovery of allosteric drugs using structure-based computational methods.

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Achieving room-temperature superconductivity is an important goal in chemistry and physics. Excitingly, pressure-induced superconducting hydrides, a typical representative of LaH₁₀ with a critical temperature (T_c) of 250–260 K around 180–200 GPa, bring this goal within reach, igniting an irresistible wave of discovering new H-containing superconductors. Moreover, this breakthrough finding was achieved under the guidance of theoretical prediction. Thus far, the superconductivity of binary hydrides has been extensively explored. However, the high-temperature superconductor, facilitating practical application, is still rare. Ternary hydrides can provide more abundant structures resulting from diverse chemical compositions and synergistic charge transfer, combine the merits of different elements, and induce strong electron–phonon coupling, which make them an appealing contender for superconductors. Recently, much research progress has been made in pressure-induced superconducting ternary hydrides. In this regard, we summarize the recent development of superconducting ternary hydrides, highlighting the chemical composition, structure, pressure, and T_c value as well as the study of doping/substitution on the known superconducting binary hydrides. The recent state-of-the-art of theoretical approaches for predicting superconductors and fundamental characters of ternary hydrides with high T_c are outlined. On the other hand, the problems, challenges, and opportunities are presented, providing an outlook for future research.

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Chemical bonding is at heart but, not being a quantum mechanical-defined physical property of a system, is a subject of endless and often fruitless debates. Having so many and very different models of chemical bonding without knowing what this really is does not make it easier. There is, however, a general agreement that concentrating electron density (ED) in and delocalizing ED to internuclear region is always associated with minimizing system's energy and synonymous with chemical bonding. Fragment, atomic, localized, delocalized, and interatomic (FALDI)-based density analysis involves entire space occupied by a molecule. From this molecular-wide and density-based methodology, it is possible to quantify localized and delocalized by all atoms ED at any coordinate r, including critical points on Bader's molecular graphs. Each atom and atom-pair contributions of delocalized density are quantified to reveal major players in the all-atom and molecular-wide chemical bonding. Partitioning the total ED to individual molecular or natural orbital's contributions using MO-ED and MO-DI methods, in conjunction with one dimensional (1D) cross section methodology, generates an orbital-based molecular-wide picture. This provides, besides reproducing results from FALDI, qualitative description of orbitals' nature that correlates well with classical understanding of bonding, nonbonding, and antibonding orbitals. A qualitative and quantitative impact of an immediate, distant, or molecular-wide molecular environment on intra- and intermolecular di-atomic, intra- and interfragment interactions is the domain of the Fragment Attributed Molecular System Energy Change (FAMSEC) family of methods. The FALDI, FAMSEC, MO-ED, MO-DI, and 1D cross section methodologies provide consistent and quantifiable physics-based picture of molecular-wide chemical bonding without invoking unicorns, such as a chemical bond.

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De novo drug design is a stationary way to build novel ligands in the confined pocket of receptor by assembling the atoms or fragments, while molecular dynamics (MD) simulation is a dynamical way to study the interaction mechanism between the ligands and receptors based on the molecular force field. De novo drug design and MD simulation are effective tools for novel drug discovery. With the development of technology, deep learning methods, and interpretable machine learning (IML) have emerged in the research area of drug design. Deep learning methods and IML can be used further to improve the efficiency and accuracy of de novo drug design and MD simulations. The application summary of deep learning methods for de novo drug design, MD simulations, and IML can further promote the technical development of drug discovery. In this article, two major workflow methods and the related components of classical algorithm and deep learning are described for de novo drug design from a new perspective. The application progress of deep learning is also summarized for MD simulations. Furthermore, IML is introduced for the deep learning model interpretability of de novo drug design and MD simulations. Our paper deals with an interesting topic about deep learning applications of de novo drug design and MD simulations for the scientific community.

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The cover image is based on the Advanced Review Computational investigations of polymerase enzymes: Structure, function, inhibition, and biotechnology by Inacrist Geronimo et al., https://doi.org/10.1002/wcms.1534.

Structure and dynamics of electronic excited states of molecular systems is of contemporary interest to understand a rich variety of processes ranging from molecular spectroscopy to reaction dynamics. Developments over the past decades have established that coupling of electronic and nuclear motion is a generic feature in polyatomic molecular systems which causes a breakdown of the Born–Oppenheimer approximation. Such a coupling leads to conical intersections in non-linear and glancing degeneracy in linear polyatomic systems. While the former case is quite general and has wide variances depending on the nature of the coupling on the symmetry ground, the latter one is specific to the linear systems having axial symmetry. The Jahn–Teller (JT) systems form a subclass in the former category and the latter category constitutes the Renner–Teller (RT) systems. These systems are characterized by high point group symmetry and orbital degeneracy. The latter splits upon distortion along suitable symmetry reducing nuclear displacement. The JT or the RT split component states may undergo symmetry allowed coupling among another JT or RT split component states or with some non-degenerate states. These are termed as the pseudo-JT and pseudo-RT coupling, respectively. We have studied a variety of photo-induced molecular processes with the aid of ab initio quantum chemistry and first principles quantum dynamics methods. We will document here some of the important developments in the field focusing on the work done on the JT and RT systems primarily in our own laboratory.

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Molecular dynamics (MD) simulation is a powerful tool for exploring the conformational energy landscape of proteins, and the reliability of MD results is crucially dependent on the underlying force field (FF). An accurate FF capable of producing balanced distributions of diverse conformations at multiple levels has been a long-sought goal. Towards this, several decades of joint efforts have been made to address FF deficiencies, manifested by conformational biases at different levels (local conformations, secondary structures, and global extendedness of polypeptide chain). We first present the major FF biases, then review the strategies to address them separately. Specifically, both nonresidue-specific and residue-specific strategies for torsional parameter optimization have been applied to achieve local conformation and secondary structure balances. Significant improvements can be gained with residue-specific torsional parameters especially when explicit dihedral couplings are considered. Further, the additional balance between protein–protein and protein–water interactions has been optimized via multiple ways to reproduce the global extendedness of polypeptide chains, especially for unfolded or disordered proteins. This review aims to summarize the most valuable experience and lessons gained from the past, which, we hope, can facilitate further improvements of both classical FFs and more sophisticated models such as polarizable FFs.

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