Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

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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For the simulations of polymeric systems, coarse-grained (CG) molecular dynamics simulations are computationally demanding not only because of their high computational efficiency, but also these CG models can provide sufficient structural and dynamical properties at both micro- and meso-scopic levels. During the past decades, developments of these CG models are roughly in two directions, that is, generic and chemically system-specific models. The developme of the formmer focuses on the capability of the model to capature the general properties of the system, for instance, scaling relations between both structural and dynamic properties with respect to chain length. On the other hand, to bridging the gap between physics and chemistry, chemically-specifi models are also widely developed which are able to retain the inherent chemical–physical properties for a given polymer system. However, due to the reduction of atomistic degree of freedom a faithful reproduction of structure and especialy dynamics properties of the system is the maijor challenge. In this review, after a brief introduction of some widely used generic models, we present an overview of both recent achievements and remainning challendges in the development of chemically-specific CG approaches, for the simulations of polymer systems.

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Designing functional molecules is the prerogative of experts who have advanced knowledge and experience in their fields. To democratize automatic molecular design for both experts and nonexperts, we introduce a generic open-sourced framework, ChemTSv2, to design molecules based on a de novo molecule generator equipped with an easy-to-use interface. Besides, ChemTSv2 can easily be integrated with various simulation packages, such as Gaussian 16 package, and supports a massively parallel exploration that accelerates molecular designs. We exhibit the potential of molecular design with ChemTSv2, including previous work, such as chromophores, fluorophores, drugs, and so forth. ChemTSv2 contributes to democratizing inverse molecule design in various disciplines relevant to chemistry.

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Artificial intelligence (AI) is having a growing impact in many areas related to drug discovery. However, it is still critical for their adoption by the medicinal chemistry community to achieve models that, in addition to achieving high performance in their predictions, can be trusty explained to the end users in terms of their knowledge and background. Therefore, the investigation and development of explainable artificial intelligence (XAI) methods have become a key topic to address this challenge. For this reason, a comprehensive literature review about explanation methodologies for AI based models, focused in the field of drug discovery, is provided. In particular, an intuitive overview about each family of XAI approaches, such as those based on feature attribution, graph topologies, or counterfactual reasoning, oriented to a wide audience without a strong background in the AI discipline is introduced. As the main contribution, we propose a new taxonomy of the current XAI methods, which take into account specific issues related with the typical representations and computational problems study in the design of molecules. Additionally, we also present the main visualization strategies designed for supporting XAI approaches in the chemical domain. We conclude with key ideas about each method category, thoroughly providing insightful analysis about the guidelines and potential benefits of their adoption in medical chemistry.

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