Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

Catalysis stands as a cornerstone for the global economy and human society, with metals and metal oxides assuming significant roles in catalytic research. The emergence of two-dimensional (2D) carbon materials, such as graphene (GR), graphyne (GY), and graphdiyne (GDY), boasting unique structural and tunable electronic properties, opens up new avenues for the exploration of heterogeneous catalysts. In this review, we initially analyze the limitations inherent in metal- and metal oxide-based catalysts. Subsequently, we present an overview of the latest advancements in heterogeneous catalysts pertaining to 2D carbon-metal composites. We categorize these composites into two groups: support-induced catalysts with disordered lattices and metal-carbon crystals. The realm of 2D support-induced catalysts predominantly encompasses GR-, GY-, and GDY-supported single-atom catalysts (SACs), dual-atom catalysts (DACs), and single-cluster catalysts (SCCs). Meanwhile, the domain of 2D metal-carbon crystals primarily includes metal organic frameworks (MOFs), transition metal carbides (MXenes), and graphite metal carbides (g-MCs). This review encapsulates a comprehensive understanding of the structure, stability, and catalytic application of all these 2D carbon-metal composites from a theoretical standpoint, placing particular emphasis on the coordination structure –performance relationship. To conclude, a brief summary and outlook are provided, offering insights for the future study of 2D carbon-metal composites.

Proteolysis targeting chimera (PROTAC) induces specific protein degradation through the ubiquitin–proteasome system and offers significant advantages over small molecule drugs. They are emerging as a promising avenue, particularly in targeting previously “undruggable” targets. Traditional PROTACs have been discovered through large-scale experimental screening. Extensive research efforts have been focused on unraveling the biological and pharmacological functions of PROTACs, with significant strides made toward transitioning from empirical discovery to rational, structure-based design strategies. This review provides an overview of recent representative computer-aided drug design studies focused on PROTACs. We highlight how the utilization of the targeted protein degradation database, molecular modeling techniques, machine learning algorithms, and computational methods contributes to facilitating PROTAC discovery. Furthermore, we conclude the achievements in the PROTAC field and explore challenges and future directions. We aim to offer insights and references for future computational studies and the rational design of PROTACs.

Deep learning has deeply changed the paradigms of many research fields. At the heart of chemical and physical sciences is the accurate ab initio calculation of many-body wavefunctions, which has become one of the most notable examples to demonstrate the power of deep learning in science. In particular, the introduction of deep learning into quantum Monte Carlo (QMC) has significantly advanced the frontier of ab initio calculation, offering a universal tool to solve the electronic structure of materials and molecules. Deep learning QMC architectures were initially designed and tested on small molecules, focusing on comparisons with other state-of-the-art ab initio methods. Methodological developments, including extensions to real solids and periodic models, have been rapidly progressing, and reported applications are fast expanding. This review covers the theoretical foundation of deep learning QMC for solids, the neural network wavefunction ansatz, and various other methodological developments. Applications on computing energy, electron density, electric polarization, force, and stress of real solids are also reviewed. The methods have also been extended to other periodic systems and finite temperature calculations. The review highlights the potential and existing challenges of deep learning QMC in materials chemistry and condensed matter physics.

With the increasing global demand for energy transition and environmental sustainability, catalysts play a vital role in mitigating global climate change, as they facilitate over 90% of chemical and material conversions. It is important to investigate the complex structures and properties of catalysts for enhanced performance, for which artificial intelligence (AI) methods, especially graph neural networks (GNNs) could be useful. In this article, we explore the cutting-edge applications and future potential of GNNs in intelligent catalyst design. The fundamental theories of GNNs and their practical applications in catalytic material simulation and inverse design are first reviewed. We analyze the critical roles of GNNs in accelerating material screening, performance prediction, reaction pathway analysis, and mechanism modeling. By leveraging graph convolution techniques to accurately represent molecular structures, integrating symmetry constraints to ensure physical consistency, and applying generative models to efficiently explore the design space, these approaches work synergistically to enhance the efficiency and accuracy of catalyst design. Furthermore, we highlight high-quality databases crucial for catalysis research and explore the innovative application of GNNs in thermocatalysis, electrocatalysis, photocatalysis, and biocatalysis. In the end, we highlight key directions for advancing GNNs in catalysis: dynamic frameworks for real-time conditions, hierarchical models linking atomic details to catalyst features, multi-task networks for performance prediction, and interpretability mechanisms to reveal critical reaction pathways. We believe these advancements will significantly broaden the role of GNNs in catalysis science, paving the way for more efficient, accurate, and sustainable catalyst design methodologies.

Protein–protein interactions play a crucial role in human biological processes, and deciphering their structural information and interaction patterns is essential for drug development. The high costs of experimental structure determination have brought computational protein–protein docking methods into the spotlight. Traditional docking algorithms, which hinge on a sampling-scoring framework, heavily rely on extensive sampling of candidate poses and customized scoring functions based on the geometric and chemical compatibility between proteins. However, these methods face challenges related to sampling efficiency and stability. The advent of deep learning (DL) has ushered in data-driven docking methods that demonstrate significant advantages, particularly boosting the efficiency of protein–protein docking. We systematically review the historical development of protein–protein docking from traditional approaches to DL techniques and provide insights into emerging technologies in this field. Moreover, we summarize the commonly used datasets and evaluation metrics in protein–protein docking. We expect that this review can offer valuable guidance for the development of more efficient protein–protein docking algorithms.

Recent advances in single-atom alloy (SAA) catalysts provide a unique platform for understanding spillover, due to the well-defined nature of the active site for dissociative chemisorption. In particular, the use of spilled adsorbates following molecular dissociation on the host metal surface facilitates the generation of high-value chemicals in subsequent catalytic reactions. Nevertheless, the factors that control the spillover process remain to be fully elucidated. This perspective discusses recent theoretical advances in the spillover dynamics on SAAs, with a particular focus on the dissociation and spillover processes of H₂ and CH₄. It provides valuable insights into how various factors, such as energy transfer, nuclear quantum effects, gas-adsorbate interactions, and adsorbate size, impact the diffusion behavior of hydrogen and methyl species on SAA surfaces. The article concludes with a discussion of future prospects. This perspective underscores the significance of spillover dynamics in heterogeneous catalysis, with important implications for improving catalytic performance.

We describe our contribution as industrial stakeholders to the existing open-source GPU4PySCF project (https://github.com/pyscf/gpu4pyscf), a GPU-accelerated Python quantum chemistry package. We have integrated GPU acceleration into other PySCF functionalities including Density Functional Theory (DFT), geometry optimization, frequency analysis, solvent models, and the density fitting technique. Through these contributions, GPU4PySCF v1.0 can now be regarded as a fully functional and industrially relevant platform, which we demonstrate in this work through a range of tests. When performing DFT calculations with the density fitting scheme on modern GPU platforms, GPU4PySCF delivers a 30 times speedup over a 32-core CPU node, resulting in approximately 90% cost savings for most DFT tasks. The performance advantages and productivity improvements have been found in multiple industrial applications, such as generating potential energy surfaces, analyzing molecular properties, calculating solvation free energy, identifying chemical reactions in lithium-ion batteries, and accelerating neural-network methods. With the improved design that makes it easy to integrate with the Python and PySCF ecosystem, GPU4PySCF is a natural choice that we can now recommend for many industrial quantum chemistry applications.