Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

Accurate and rapid prediction of protein–ligand interactions (PLIs) is the fundamental challenge of drug discovery. Deep learning methods have been harnessed for this purpose, yet the insufficient generalizability of PLI prediction prevents their broader impact on practical applications. Here, we highlight the significance of PLI model generalizability by conceiving PLIs as a function defined on infinitely diverse protein–ligand pairs and binding poses. To delve into the generalization challenges within PLI predictions, we comprehensively explore the evaluation strategies to assess the generalizability fairly. Moreover, we categorize structure-based PLI models with leveraged strategies for learning generalizable features from structure-based PLI data. Finally, we conclude the review by emphasizing the need for accurate pose-predicting methods, which is a prerequisite for more accurate PLI predictions.

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Pedersen TB, Lehtola S, Fdez. Galván I, Lindh R. The versatility of the Cholesky decomposition in electronic structure theory. WIREs Comput Mol Sci. 2024; 14(1):e1692. https://doi.org/10.1002/wcms.1692.

We apologize for this error and thank Prof. L. De Vico for bringing this to our attention.

Amyloid proteins are characterized by their tendency to aggregate into amyloid fibrils, which are often associated with devastating diseases. Aggregation pathways typically involve unfolding or misfolding of monomeric proteins and formation of transient oligomers and protofibrils before the final aggregation product is formed. The conformational dynamics and polymorphic and volatile nature of these aggregation intermediates make their characterization by experimental techniques alone insufficient and also require computational approaches. Over the past 25 years, the size of simulated amyloid aggregation systems and the length of these simulations have increased significantly. These advances are discussed here. The review includes simulation approaches that model the aggregating peptides or proteins at both the all-atom and coarse-grained levels, use molecular dynamics simulations or Monte Carlo sampling to simulate the conformational changes, and present results for various amyloid peptides and proteins ranging from Lys-Phe-Phe-Glu (KFFE) as the smallest system to $��\mathrm{A\beta }�$ as an intermediate-sized peptide to α-synuclein. The presentation of the history of amyloid aggregation simulations concludes with a discussion of where the future of these simulations may lie.

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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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