Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

Potassium (K⁺) channels play vital roles in various physiological functions, including regulating K⁺ flow in cell membranes, impacting nervous system signal transduction, neuronal firing, muscle contraction, neurotransmitters, and enzyme secretion. Their activation and switch-off are directly linked to diseases like arrhythmias, atrial fibrillation, and pain etc. Although the experimental methods play important roles in the studying the structure and function of K⁺ channels, they are still some limitations to enclose the dynamic molecular processes and the corresponding mechanisms of conformational changes during ion transport, permeation, and gating control. Relatively, computational methods have obvious advantages in studying such problems compared with experimental methods. Recently, more and more three-dimensional structures of K⁺ channels have been disclosed based on experimental methods and in silico prediction methods, which provide a good chance to study the molecular mechanism of conformational changes related to the functional regulations of K⁺ channels. Based on these structural details, molecular dynamics simulations together with related methods such as enhanced sampling and free energy calculations, have been widely used to reveal the conformational dynamics, ion conductance, ion channel gating, and ligand binding mechanisms. Additionally, the accessibility of structures also provides a large space for structure-based drug design. This review mainly addresses the recent progress of computational methods in the structure, mechanism, and drug design of K⁺ channels. After summarizing the progress in these fields, we also give our opinion on the future direction in the area of K⁺ channel research combined with the cutting edge of computational methods.

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Planar hypercoordinate compounds are fascinating but challenging to be realized. The difficulty in stabilizing and fabricating such compounds prevent us from in-deep understanding these compounds and exploring potential applications. Molecular-level insights on underlying mechanism for the formation of viable hypercoordinate compounds is the key towards the development of this field. This review aims to summarize recent advances in this direction. Regular polygons AL_CN (A and L are central and ligand atoms, CN is coordination number) are generally applicable models used to derive the unified mathematical relations between the radii of constitute atoms and the angles of regular polygons as exemplified by two typical examples Gr14L_CN and TMB_CN (Gr14 is Group 14 element, TM is transition metal, B is boron). Effective schemes and some useful rule of thumb are proposed towards the architecture of 2D hypercoordinate crystals AL_x (x is composition ratio). A set of design flow chart and several effective design strategies and principles are suggested for 2D-HyperMaters. Potential diverse applications of 2D-HyperMaters are discussed and summarized. Grand blueprint for planar hypercoordinate chemistry is drew. Finally, future prospects of 2D-HyperChem is outlooked.

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The past years since the publication of our review on subsystem density-functional theory (sDFT) (WIREs Comput Mol Sci. 2014, 4:325–362) have witnessed a rapid development and diversification of quantum mechanical fragmentation and embedding approaches related to sDFT and frozen-density embedding (FDE). In this follow-up article, we provide an update addressing formal and algorithmic work on sDFT/FDE, novel approximations developed for treating the non-additive kinetic energy in these DFT/DFT hybrid methods, new areas of application and extensions to properties previously not accessible, projection-based techniques as an alternative to solely density-based embedding, progress in wavefunction-in-DFT embedding, new fragmentation strategies in the context of DFT which are technically or conceptually similar to sDFT, and the blurring boundary between advanced DFT/MM and approximate DFT/DFT embedding methods.

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Having made significant advancements in understanding living organisms at various levels such as genes, cells, molecules, tissues, and pathways, the field of life sciences is now shifting towards integrating these components into the bigger picture to understand their collective behavior. Such a shift of perspective requires a general conceptual framework for understanding complexity in life sciences which is currently elusive, a transition being facilitated by large-scale data collection, unprecedented computational power, and new analytical tools. In recent years, life sciences have been revolutionized with AI methods, and quantum computing is touted to be the next most significant leap in technology. Here, we provide a theoretical framework to orient researchers around key concepts of how quantum computing can be integrated into the study of the hierarchical complexity of living organisms and discuss recent advances in quantum computing for life sciences.

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The two-electron reduced density matrix (2RDM) carries enough information to evaluate the electronic energy of a many-electron system. The variational 2RDM (v2RDM) approach seeks to determine the 2RDM directly, without knowledge of the wave function, by minimizing this energy with respect to variations in the elements of the 2RDM, while also enforcing known N-representability conditions. In this tutorial review, we provide an overview of the theoretical underpinnings of the v2RDM approach and the N-representability constraints that are typically applied to the 2RDM. We also discuss the semidefinite programming (SDP) techniques used in v2RDM computations and provide enough Python code to develop a working v2RDM code that interfaces to the libSDP library of SDP solvers.

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The cover image is based on the Advanced Review Simplifi ed quantum chemistry methods to evaluate non-linear optical properties of large systems by Sarah Löffelsender et al., https://doi.org/10.1002/wcms.1695

Molecules are complex dynamic objects that can exist in different molecular forms (conformations, tautomers, stereoisomers, protonation states, etc.) and often it is not known which molecular form is responsible for observed physicochemical and biological properties of a given molecule. This raises the problem of the selection of the correct molecular form for machine learning modeling of target properties. The same problem is common to biological molecules (RNA, DNA, proteins)—long sequences where only key segments, which often cannot be located precisely, are involved in biological functions. Multi-instance machine learning (MIL) is an efficient approach for solving problems where objects under study cannot be uniquely represented by a single instance, but rather by a set of multiple alternative instances. Multi-instance learning was formalized in 1997 and motivated by the problem of conformation selection in drug activity prediction tasks. Since then MIL has found a lot of applications in various domains, such as information retrieval, computer vision, signal processing, bankruptcy prediction, and so on. In the given review we describe the MIL framework and its applications to the tasks associated with ambiguity in the representation of small and biological molecules in chemoinformatics and bioinformatics. We have collected examples that demonstrate the advantages of MIL over the traditional single-instance learning (SIL) approach. Special attention was paid to the ability of MIL models to identify key instances responsible for a modeling property.

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Ultrafast electron dynamics have made rapid progress in the last few years. With Jellyfish, we now introduce a program suite that enables to perform the entire workflow of an electron-dynamics simulation. The modular program architecture offers a flexible combination of different propagators, Hamiltonians, basis sets, and more. Jellyfish can be operated by a graphical user interface, which makes it easy to get started for nonspecialist users and gives experienced users a clear overview of the entire functionality. The temporal evolution of a wave function can currently be executed in the time-dependent configuration interaction method (TDCI) formalism, however, a plugin system facilitates the expansion to other methods and tools without requiring in-depth knowledge of the program. Currently developed plugins allow to include results from conventional electronic structure calculations as well as the usage and extension of quantum-compute algorithms for electron dynamics. We present the capabilities of Jellyfish on three examples to showcase the simulation and analysis of light-driven correlated electron dynamics. The implemented visualization of various densities enables an efficient and detailed analysis for the long-standing quest of the electron–hole pair formation.

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Peptide-based drugs offer high specificity, potency, and selectivity. However, their inherent flexibility and differences in conformational preferences between their free and bound states create unique challenges that have hindered progress in effective drug discovery pipelines. The emergence of AlphaFold (AF) and Artificial Intelligence (AI) presents new opportunities for enhancing peptide-based drug discovery. We explore recent advancements that facilitate a successful peptide drug discovery pipeline, considering peptides' attractive therapeutic properties and strategies to enhance their stability and bioavailability. AF enables efficient and accurate prediction of peptide-protein structures, addressing a critical requirement in computational drug discovery pipelines. In the post-AF era, we are witnessing rapid progress with the potential to revolutionize peptide-based drug discovery such as the ability to rank peptide binders or classify them as binders/non-binders and the ability to design novel peptide sequences. However, AI-based methods are struggling due to the lack of well-curated datasets, for example to accommodate modified amino acids or unconventional cyclization. Thus, physics-based methods, such as docking or molecular dynamics simulations, continue to hold a complementary role in peptide drug discovery pipelines. Moreover, MD-based tools offer valuable insights into binding mechanisms, as well as the thermodynamic and kinetic properties of complexes. As we navigate this evolving landscape, a synergistic integration of AI and physics-based methods holds the promise of reshaping the landscape of peptide-based drug discovery.

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This review presents the theoretical background concerning simplified quantum chemistry (sQC) methods to compute non-linear optical (NLO) properties and their applications to large systems. To evaluate any NLO responses such as hyperpolarizabilities or two-photon absorption (2PA), one should evidently perform first a ground state calculation and compute its response. Because of this, methods used to compute ground states of large systems are outlined, especially the xTB (extended tight-binding) scheme. An overview on approaches to compute excited state and response properties is given, emphasizing the simplified time-dependent density functional theory (sTD-DFT). The formalism of the eXact integral sTD-DFT (XsTD-DFT) method is also introduced. For the first hyperpolarizability, 2PA, excited state absorption, and second hyperpolarizability, a brief historical review is given on early-stage semi-empirical method applications to systems that were considered large at the time. Then, we showcase recent applications with sQC methods, especially the sTD-DFT scheme to large challenging systems such as fluorescent proteins or fluorescent organic nanoparticles as well as dynamic structural effects on flexible tryptophan-rich peptides and gramicidin A. Thanks to the sTD-DFT-xTB scheme, all-atom quantum chemistry methodologies are now possible for the computation of the first hyperpolarizability and 2PA of systems up to 5000 atoms. This review concludes by summing-up current and future method developments in the sQC framework as well as forthcoming applications on large systems.

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